Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Alth...Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Although several active semi-supervised fuzzy clustering methods have been developed previously,they typically face significant limitations,including high computational complexity,sensitivity to initial cluster centroids,and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters.This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify,analyze,and correct misclassified boundary elements.By strategically utilizing labeled data through active learning,our method improves the robustness and precision of cluster boundary assignments.Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets,synthetic data with controlled boundary overlap,and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods.The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.展开更多
Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While s...Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.展开更多
Attribute-graph clustering aims to divide the graph nodes into distinct clusters in an unsupervised manner,which usually encodes the node attribute feature and the corresponding graph structure into a latent feature s...Attribute-graph clustering aims to divide the graph nodes into distinct clusters in an unsupervised manner,which usually encodes the node attribute feature and the corresponding graph structure into a latent feature space.However,traditional attribute-graph clustering methods often neglect the effect of neighbor information on clustering,leading to suboptimal clustering results as they fail to fully leverage the rich contextual information provided by neighboring nodes,which is crucial for capturing the intrinsic relationships between nodes and improving clustering performance.In this paper,we propose a novel Neighbor Dual-Consistency Constrained Attribute-Graph Clustering that leverages information from neighboring nodes in two significant aspects:neighbor feature consistency and neighbor distribution consistency.To enhance feature consistency among nodes and their neighbors,we introduce a neighbor contrastive loss that encourages the embeddings of nodes to be closer to those of their similar neighbors in the feature space while pushing them further apart from dissimilar neighbors.This method helps the model better capture local feature information.Furthermore,to ensure consistent cluster assignments between nodes and their neighbors,we introduce a neighbor distribution consistency module,which combines structural information from the graph with similarity of attributes to align cluster assignments between nodes and their neighbors.By integrating both local structural information and global attribute information,our approach effectively captures comprehensive patterns within the graph.Overall,our method demonstrates superior performance in capturing comprehensive patterns within the graph and achieves state-of-the-art clustering results on multiple datasets.展开更多
Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize cl...Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive research.Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties.Noisy data can lead to incorrect object recognition and inference.This research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data.The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI datasets.The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.展开更多
In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world da...In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.展开更多
Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning.This paper implements and compares unsupervised and semi-supervise...Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning.This paper implements and compares unsupervised and semi-supervised clustering analysis of BOA-Argo ocean text data.Unsupervised K-Means and Affinity Propagation(AP)are two classical clustering algorithms.The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range.Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition,and use this data for semi-supervised cluster analysis.Several semi-supervised clustering algorithms were chosen for comparison of learning performance:Constrained-K-Means,Seeded-K-Means,SAP(Semi-supervised Affinity Propagation),LSAP(Loose Seed AP)and CSAP(Compact Seed AP).In order to adapt the single label,this paper improves the above algorithms to SCKM(improved Constrained-K-Means),SSKM(improved Seeded-K-Means),and SSAP(improved Semi-supervised Affinity Propagationg)to perform semi-supervised clustering analysis on the data.A DSAP(Double Seed AP)semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering effect.The unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.展开更多
With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,...With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.展开更多
Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.The...Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.Therefore,measuring the distance between sample points is crucial to the effectiveness of clustering.Filtering features by label information and mea-suring the distance between samples by these features is a common supervised learning method to reconstruct distance metric.However,in many application scenarios,it is very expensive to obtain a large number of labeled samples.In this paper,to solve the clustering problem in the few supervised sample and high data dimensionality scenarios,a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information,such as Must-Link and Cannot-Link,and then cluster the data in the new metric space.The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping.Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm.Average clustering metrics on various datasets improved by 8%compared to the comparison algorithm.展开更多
A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered...A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered combination, divides an affinity propagation clustering( APC) process into several hierarchies evenly,draws samples from data of each hierarchy according to weight,and executes semi-supervised learning through construction of pairwise constraints and use of submanifold label mapping,weighting and combining clustering results of all hierarchies by combined promotion. It is shown by theoretical analysis and experimental result that clustering accuracy and computation complexity of the semi-supervised affinity propagation clustering algorithm based on layered combination( SAP-LC algorithm) have been greatly improved.展开更多
In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore,...In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster division.Experimental results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.展开更多
Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at ...Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.展开更多
Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin s...Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).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
Cell-free networks can effectively reduce interference due to diversity gain.Two key technologies,access point(AP)clustering and transceiver design,play key roles in cell-free networks,and they are implemented at diff...Cell-free networks can effectively reduce interference due to diversity gain.Two key technologies,access point(AP)clustering and transceiver design,play key roles in cell-free networks,and they are implemented at different layers of the air interface.To address the issues and obtain global optimal results,this paper proposes an uplink joint AP clustering and receiver optimization algorithm,where a cross-layer optimization model is built based on graph neural networks(GNNs)with low computational complexity.Experimental results show that the proposed algorithm can activate fewer APs for each user with a small performance loss compared with conventional algorithms.展开更多
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.展开更多
To construct a high efficient text clustering algorithm the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed. The model is applied to text clustering. The performance of ...To construct a high efficient text clustering algorithm the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed. The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application. The experiment result demonstrated that the multilevel graph text clustering algorithm is available. Key words text clustering - multilevel coarsen graph model - refinement algorithm - high-dimensional clustering CLC number TP301 Foundation item: Supported by the National Natural Science Foundation of China (60173051)Biography: CHEN Jian-bin(1970-), male, Associate professor, Ph. D., research direction: data mining.展开更多
How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we ...How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we proposed a new method to extract communities. The MCL algorithm, which is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm is used to extract communities. By putting mirror deleting procedure behind graph clustering, we decrease comparing cost considerably. After MCL and mirror deletion, we use community member select algorithm to produce the sets of community candidates. The experiment and results show the new method works effectively and properly.展开更多
With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasin...With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly prominent.Thus,it is crucial to detect anomalies in the collected IoT time series from various sensors.Recently,deep learning models have been leveraged for IoT anomaly detection.However,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning techniques.Nevertheless,the absence of accurate abnormal information in unsupervised learning methods limits their performance.To address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly identification.It performs better than unsupervised methods using only a small amount of labeled data.Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model.However,the dependencies between data are often unknown in time series data.To solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series data.It not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key data.Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.展开更多
The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or under...The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.展开更多
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to bes...Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.展开更多
文摘Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Although several active semi-supervised fuzzy clustering methods have been developed previously,they typically face significant limitations,including high computational complexity,sensitivity to initial cluster centroids,and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters.This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify,analyze,and correct misclassified boundary elements.By strategically utilizing labeled data through active learning,our method improves the robustness and precision of cluster boundary assignments.Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets,synthetic data with controlled boundary overlap,and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods.The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.
文摘Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.
基金supported by National Natural Science Foundation of China(Nos.62272015,62441232).
文摘Attribute-graph clustering aims to divide the graph nodes into distinct clusters in an unsupervised manner,which usually encodes the node attribute feature and the corresponding graph structure into a latent feature space.However,traditional attribute-graph clustering methods often neglect the effect of neighbor information on clustering,leading to suboptimal clustering results as they fail to fully leverage the rich contextual information provided by neighboring nodes,which is crucial for capturing the intrinsic relationships between nodes and improving clustering performance.In this paper,we propose a novel Neighbor Dual-Consistency Constrained Attribute-Graph Clustering that leverages information from neighboring nodes in two significant aspects:neighbor feature consistency and neighbor distribution consistency.To enhance feature consistency among nodes and their neighbors,we introduce a neighbor contrastive loss that encourages the embeddings of nodes to be closer to those of their similar neighbors in the feature space while pushing them further apart from dissimilar neighbors.This method helps the model better capture local feature information.Furthermore,to ensure consistent cluster assignments between nodes and their neighbors,we introduce a neighbor distribution consistency module,which combines structural information from the graph with similarity of attributes to align cluster assignments between nodes and their neighbors.By integrating both local structural information and global attribute information,our approach effectively captures comprehensive patterns within the graph.Overall,our method demonstrates superior performance in capturing comprehensive patterns within the graph and achieves state-of-the-art clustering results on multiple datasets.
基金This research is funded by Graduate University of Science and Technology under grant number GUST.STS.DT2020-TT01。
文摘Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive research.Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties.Noisy data can lead to incorrect object recognition and inference.This research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data.The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI datasets.The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.
基金supported in part by the National Natural Science Foundation of China under Grant 62171203in part by the Jiangsu Province“333 Project”High-Level Talent Cultivation Subsidized Project+2 种基金in part by the SuzhouKey Supporting Subjects for Health Informatics under Grant SZFCXK202147in part by the Changshu Science and Technology Program under Grants CS202015 and CS202246in part by Changshu Key Laboratory of Medical Artificial Intelligence and Big Data under Grants CYZ202301 and CS202314.
文摘In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.
基金This work was supported in part by the National Natural Science Foundation of China(51679105,61872160,51809112)“Thirteenth Five Plan”Science and Technology Project of Education Department,Jilin Province(JJKH20200990KJ).
文摘Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning.This paper implements and compares unsupervised and semi-supervised clustering analysis of BOA-Argo ocean text data.Unsupervised K-Means and Affinity Propagation(AP)are two classical clustering algorithms.The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range.Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition,and use this data for semi-supervised cluster analysis.Several semi-supervised clustering algorithms were chosen for comparison of learning performance:Constrained-K-Means,Seeded-K-Means,SAP(Semi-supervised Affinity Propagation),LSAP(Loose Seed AP)and CSAP(Compact Seed AP).In order to adapt the single label,this paper improves the above algorithms to SCKM(improved Constrained-K-Means),SSKM(improved Seeded-K-Means),and SSAP(improved Semi-supervised Affinity Propagationg)to perform semi-supervised clustering analysis on the data.A DSAP(Double Seed AP)semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering effect.The unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61101122 and 61071105)
文摘With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.
文摘Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.Therefore,measuring the distance between sample points is crucial to the effectiveness of clustering.Filtering features by label information and mea-suring the distance between samples by these features is a common supervised learning method to reconstruct distance metric.However,in many application scenarios,it is very expensive to obtain a large number of labeled samples.In this paper,to solve the clustering problem in the few supervised sample and high data dimensionality scenarios,a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information,such as Must-Link and Cannot-Link,and then cluster the data in the new metric space.The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping.Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm.Average clustering metrics on various datasets improved by 8%compared to the comparison algorithm.
基金the Science and Technology Research Program of Zhejiang Province,China(No.2011C21036)Projects in Science and Technology of Ningbo Municipal,China(No.2012B82003)+1 种基金Shanghai Natural Science Foundation,China(No.10ZR1400100)the National Undergraduate Training Programs for Innovation and Entrepreneurship,China(No.201410876011)
文摘A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered combination, divides an affinity propagation clustering( APC) process into several hierarchies evenly,draws samples from data of each hierarchy according to weight,and executes semi-supervised learning through construction of pairwise constraints and use of submanifold label mapping,weighting and combining clustering results of all hierarchies by combined promotion. It is shown by theoretical analysis and experimental result that clustering accuracy and computation complexity of the semi-supervised affinity propagation clustering algorithm based on layered combination( SAP-LC algorithm) have been greatly improved.
基金This research has been partially supported by the national natural science foundation of China (51175169) and the national science and technology support program (2012BAF02B01).
文摘In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster division.Experimental results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.
基金This work has been supported by.Central University Research Fund(No.2016MS116,No.2016MS117,No.2018MS074)the National Natural Science Foundation(51677072).
文摘Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.
基金supported by the National Key R&D Program of China(2023YFC3304600).
文摘Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).
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.
基金supported in part by National Natural Science Foundation of China under Grant No.62171474。
文摘Cell-free networks can effectively reduce interference due to diversity gain.Two key technologies,access point(AP)clustering and transceiver design,play key roles in cell-free networks,and they are implemented at different layers of the air interface.To address the issues and obtain global optimal results,this paper proposes an uplink joint AP clustering and receiver optimization algorithm,where a cross-layer optimization model is built based on graph neural networks(GNNs)with low computational complexity.Experimental results show that the proposed algorithm can activate fewer APs for each user with a small performance loss compared with conventional algorithms.
文摘Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
文摘To construct a high efficient text clustering algorithm the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed. The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application. The experiment result demonstrated that the multilevel graph text clustering algorithm is available. Key words text clustering - multilevel coarsen graph model - refinement algorithm - high-dimensional clustering CLC number TP301 Foundation item: Supported by the National Natural Science Foundation of China (60173051)Biography: CHEN Jian-bin(1970-), male, Associate professor, Ph. D., research direction: data mining.
基金Supported bythe 211 Project of Ministry of Educa-tion of China
文摘How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we proposed a new method to extract communities. The MCL algorithm, which is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm is used to extract communities. By putting mirror deleting procedure behind graph clustering, we decrease comparing cost considerably. After MCL and mirror deletion, we use community member select algorithm to produce the sets of community candidates. The experiment and results show the new method works effectively and properly.
基金This research is partially supported by the National Natural Science Foundation of China under Grant No.62376043Science and Technology Program of Sichuan Province under Grant Nos.2020JDRC0067,2023JDRC0087,and 24NSFTD0025.
文摘With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly prominent.Thus,it is crucial to detect anomalies in the collected IoT time series from various sensors.Recently,deep learning models have been leveraged for IoT anomaly detection.However,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning techniques.Nevertheless,the absence of accurate abnormal information in unsupervised learning methods limits their performance.To address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly identification.It performs better than unsupervised methods using only a small amount of labeled data.Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model.However,the dependencies between data are often unknown in time series data.To solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series data.It not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key data.Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.
文摘The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.
基金supported by the DOD National Defense Science and Engineering Graduate(NDSEG)Research Fellowshipsupported by the NGA under Contract No.HM04762110003.
文摘Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.
