Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregatio...Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.展开更多
Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl...Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.展开更多
The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show...The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.展开更多
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu...In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.展开更多
Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their sim...Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.展开更多
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument...Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.展开更多
Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims...Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.展开更多
The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces ...The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.展开更多
Nature-inspired optimization algorithms refer to techniques that simulate the behavior and ecosystem of living organisms or natural phenomena.One such technique is the“Photosynthesis Spectrum Algorithm,”which was de...Nature-inspired optimization algorithms refer to techniques that simulate the behavior and ecosystem of living organisms or natural phenomena.One such technique is the“Photosynthesis Spectrum Algorithm,”which was developed by mimicking the process by which photons behave as a population in plants.This optimization technique has three stages that mimic the structure of leaves and the fluorescence phenomenon.Each stage updates the fitness of the solution by using a mathematical equation to direct the photon to the reaction center.Three stages of testing have been conducted to test the efficacy of this approach.In the first stage,functions from the CEC 2019 and CEC 2021 competitions are used to evaluate the performance and convergence of the proposed method.The statistical results from non-parametric Friedman and Kendall’s W tests show that the proposed method is superior to other methods in terms of obtaining the best average of solutions and achieving stability in finding solutions.In other sections,the experiment is designed for data clustering.The proposed method is compared with recent data clustering and classification metaheuristic algorithms,indicating that this method can achieve significant performance for clustering in less than 10 s of CPU time and with an accuracy of over 90%.展开更多
A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR...A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.展开更多
Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recogni...Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.展开更多
Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algor...Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.展开更多
In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hy...In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.展开更多
Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster ...Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.展开更多
An algorithm, Clustering Algorithm Based On Sparse Feature Vector (CABOSFV),was proposed for the high dimensional clustering of binary sparse data. This algorithm compressesthe data effectively by using a tool 'Sp...An algorithm, Clustering Algorithm Based On Sparse Feature Vector (CABOSFV),was proposed for the high dimensional clustering of binary sparse data. This algorithm compressesthe data effectively by using a tool 'Sparse Feature Vector', thus reduces the data scaleenormously, and can get the clustering result with only one data scan. Both theoretical analysis andempirical tests showed that CABOSFV is of low computational complexity. The algorithm findsclusters in high dimensional large datasets efficiently and handles noise effectively.展开更多
Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the...Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of networks.The clustering process takes sensors’location and residual energy into consideration to elect suitable cluster heads.Furthermore,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself.Related experimental results display that the presented method can avoid long distance communicate between sensor nodes.Furthermore,this algorithm reduces energy consumption effectively and improves package delivery rate.展开更多
Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless ...Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.展开更多
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
基金funded by the Deanship of Scientific Research,the Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia under the project(KFU250420).
文摘Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.
文摘The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.
基金partially supported by the National Natural Science Foundation of China(62161016)the Key Research and Development Project of Lanzhou Jiaotong University(ZDYF2304)+1 种基金the Beijing Engineering Research Center of Highvelocity Railway Broadband Mobile Communications(BHRC-2022-1)Beijing Jiaotong University。
文摘In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.
文摘Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3005401)the Fundamental Research Funds for the Central Universities(Grant No.B230201013)+2 种基金the National Natural Science Foundation of China(Grants No.52309152,U2243223,and U23B20150)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220978)the Open Fund of National Dam Safety Research Center(Grant No.CX2023B03).
文摘Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.
基金This research was funded by the National Natural Science Foundation of China(Grant No.72001190)by the Ministry of Education’s Humanities and Social Science Project via the China Ministry of Education(Grant No.20YJC630173)by Zhejiang A&F University(Grant No.2022LFR062).
文摘Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.
基金sponsored by the National Natural Science Foundation of China(Nos.61972208,62102194 and 62102196)National Natural Science Foundation of China(Youth Project)(No.62302237)+3 种基金Six Talent Peaks Project of Jiangsu Province(No.RJFW-111),China Postdoctoral Science Foundation Project(No.2018M640509)Postgraduate Research and Practice Innovation Program of Jiangsu Province(Nos.KYCX22_1019,KYCX23_1087,KYCX22_1027,KYCX23_1087,SJCX24_0339 and SJCX24_0346)Innovative Training Program for College Students of Nanjing University of Posts and Telecommunications(No.XZD2019116)Nanjing University of Posts and Telecommunications College Students Innovation Training Program(Nos.XZD2019116,XYB2019331).
文摘The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.
文摘Nature-inspired optimization algorithms refer to techniques that simulate the behavior and ecosystem of living organisms or natural phenomena.One such technique is the“Photosynthesis Spectrum Algorithm,”which was developed by mimicking the process by which photons behave as a population in plants.This optimization technique has three stages that mimic the structure of leaves and the fluorescence phenomenon.Each stage updates the fitness of the solution by using a mathematical equation to direct the photon to the reaction center.Three stages of testing have been conducted to test the efficacy of this approach.In the first stage,functions from the CEC 2019 and CEC 2021 competitions are used to evaluate the performance and convergence of the proposed method.The statistical results from non-parametric Friedman and Kendall’s W tests show that the proposed method is superior to other methods in terms of obtaining the best average of solutions and achieving stability in finding solutions.In other sections,the experiment is designed for data clustering.The proposed method is compared with recent data clustering and classification metaheuristic algorithms,indicating that this method can achieve significant performance for clustering in less than 10 s of CPU time and with an accuracy of over 90%.
基金The National Natural Science Foundation of China(No.60673060)the Natural Science Foundation of Jiangsu Province(No.BK2005047)
文摘A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.
基金Supported by the Open Researches Fund Program of L IESMARS(WKL(0 0 ) 0 30 2 )
文摘Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.
文摘Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
基金supported by the National GNSS Research Center Program of the Defense Acquisition Program Administration and Agency for Defense Developmentthe Ministry of Science and ICT of the Republic of Korea through the Space Core Technology Development Program (No. NRF2018M1A3A3A02065722)
文摘In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.
基金the National Natural Science Foundation of China (Nos. 60533090 and 60603096)the National Hi-Tech Research and Development Program (863) of China (No. 2006AA010107)+2 种基金the Key Technology R&D Program of China (No. 2006BAH02A13-4)the Program for Changjiang Scholars and Innovative Research Team in University of China (No. IRT0652)the Cultivation Fund of the Key Scientific and Technical Innovation Project of MOE, China (No. 706033)
文摘Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.
文摘An algorithm, Clustering Algorithm Based On Sparse Feature Vector (CABOSFV),was proposed for the high dimensional clustering of binary sparse data. This algorithm compressesthe data effectively by using a tool 'Sparse Feature Vector', thus reduces the data scaleenormously, and can get the clustering result with only one data scan. Both theoretical analysis andempirical tests showed that CABOSFV is of low computational complexity. The algorithm findsclusters in high dimensional large datasets efficiently and handles noise effectively.
基金This work is supported by the National Natural Science Foundation of China(61772454,61811530332,61811540410,U1836208).
文摘Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of networks.The clustering process takes sensors’location and residual energy into consideration to elect suitable cluster heads.Furthermore,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself.Related experimental results display that the presented method can avoid long distance communicate between sensor nodes.Furthermore,this algorithm reduces energy consumption effectively and improves package delivery rate.
文摘Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.
