An appropriate optimal number of market segments(ONS)estimation is essential for an enterprise to achieve successful market segmentation,but at present,there is a serious lack of attention to this issue in market segm...An appropriate optimal number of market segments(ONS)estimation is essential for an enterprise to achieve successful market segmentation,but at present,there is a serious lack of attention to this issue in market segmentation.In our study,an independent adaptive ONS estimation method BWCON-NSDK-means++is proposed by integrating a newinternal validity index(IVI)Between-Within-Connectivity(BWCON)and a newstable clustering algorithmNatural-SDK-means++(NSDK-means++)in a novel way.First,to complete the evaluation dimensions of the existing IVIs,we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating the connectivity with other two commonly considered measures of compactness and separation.Then,considering the stability,number of parameters and clustering performance,we proposed the NSDK-means++to participate in the integrationwhere the natural neighbor was used to optimize the initial cluster centers(ICCs)determination strategy in the SDK-means++.At last,to ensure the objectivity of the estimatedONS,we designed a BWCON-based ONS estimation framework that does not require the user to set any parameters in advance and integrated the NSDK-means++into this framework forming a practical ONS estimation tool BWCON-NSDK-means++.The final experimental results showthat the proposed BWCONand NSDK-means++are significantlymore suitable than their respective existing models to participate in the integration for determining theONS,and the proposed BWCON-NSDK-means++is demonstrably superior to the BWCON-KMA,BWCONMBK,BWCON-KM++,BWCON-RKM++,BWCON-SDKM++,BWCON-Single linkage,BWCON-Complete linkage,BWCON-Average linkage and BWCON-Ward linkage in terms of the ONS estimation.Moreover,as an independentmarket segmentation tool,the BWCON-NSDK-means++also outperforms the existing models with respect to the inter-market differentiation and sub-market size.展开更多
In the study on functional low-carbon ergonomic validity in buildings,ergonomic validity is different from resource validity which is easy for quantitative analysis. To eliminate the complexity and uncertainty impacts...In the study on functional low-carbon ergonomic validity in buildings,ergonomic validity is different from resource validity which is easy for quantitative analysis. To eliminate the complexity and uncertainty impacts of human factors on quantitative study,it proposes a method of building a parameter of ergonomic validity—multi-effect time by using cardiotachometer to record heart rate change,being used to evaluate the functional low-carbon ergonomic validity targeting at the ontological characteristics of kitchen. This method is used to determine the physical consumption intensity( multi-effect) through heart rate incremental relation based on the principles of physiology and ergonomics,and to confirm the ergonomic validity of environmental factors by the time to complete standard work as well as multi-effect quantitative analysis. The test results show that,under the kitchen operating conditions,the multi-effect( ME) can properly reflect the real-time status of the operator and is easily operated; the parameters obtained are not significantly related to the physiological status of the operator,and multi-effect time( MT) is sensitive to the physical consumption brought about to the operator due to kitchen environmental factors; thus,it can be taken as an objective index,which is simple and easy to operate in residential kitchen functional low-carbon evaluation.展开更多
This study aimed to examine the surface and content validity of the Mentoring Function Scale for Novice Nurses, used to assess the mentoring of entry-level nurses, and to refine the scale items. In Study 1, six nurse ...This study aimed to examine the surface and content validity of the Mentoring Function Scale for Novice Nurses, used to assess the mentoring of entry-level nurses, and to refine the scale items. In Study 1, six nurse education researchers, selected using convenience sampling, with five or more years of nursing experience and experience teaching novice nurses, were invited to an expert meeting in July 2015. A group interview was conducted that lasted approximately 120 minutes. Study 2 examined the content validity index. Between September and November 2015, we distributed a self-administered questionnaire survey to 11 participants selected by convenience sampling. The participants included five nurse education researchers with a minimum of five years of nursing experience and experience teaching novice nurses, as well as six clinical nurses with a master’s degree or higher. Finally, 81 questionnaire items were retained from the initial 125 items. The 81-item Mentoring Function Scale for Novice Nurses had higher content validity than the original scale. To further increase the scale’s applicability, future studies should assess its reliability, construct validity, and criterion-related validity.展开更多
Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the ...Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.展开更多
Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initial...Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.展开更多
Given the everlasting significance of knowledge in society and academia,this article proposes a theoretical and methodological perspective on conceptualizing and investigating it.Specifically,it aims to explore the ep...Given the everlasting significance of knowledge in society and academia,this article proposes a theoretical and methodological perspective on conceptualizing and investigating it.Specifically,it aims to explore the epistemological attitude(EA)theory and its semantic approach to assessing sources of knowledge.The article provides a concise overview of the EA theory,which advocates for a systemic perspective on cognition and knowledge.It introduces and elaborates on the core concept and model,which serve as the foundation for the proposed methodology.This methodology suggests examining knowledge objects through subjective,contextual,and epistemological realms as multi-level knowledge constructs.Emphasizing the importance of semantics in studying knowledge,categories,and meanings,the article proposes an epistemological attitude towards sources of knowledge semantic questionnaire.The article delves into the methodology,reflecting on its four consecutive stages.It begins with the formal and substantive stages,which involve selecting sources,choosing academic experts as target participants,and developing content.The procedural stage follows,in which an expert review approach is employed to assess the content validity of the method.Finally,the article discusses the semantic method,elucidating its structure,features,semantic categories,and assessment procedure.The proposed method provides a unique contribution by enabling the analysis of the epistemological and socio-psychological meanings of sources,representing them as semantic constructs.展开更多
Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method b...Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm （WFCA） instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.展开更多
Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,...Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,which may not capture all the features of the data.This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance measure.Therefore,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decision-makers.Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality.The paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.展开更多
The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However...The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.展开更多
基金supported by the earmarked fund for CARS-29 and the open funds of the Key Laboratory of Viticulture and Enology,Ministry of Agriculture,China.
文摘An appropriate optimal number of market segments(ONS)estimation is essential for an enterprise to achieve successful market segmentation,but at present,there is a serious lack of attention to this issue in market segmentation.In our study,an independent adaptive ONS estimation method BWCON-NSDK-means++is proposed by integrating a newinternal validity index(IVI)Between-Within-Connectivity(BWCON)and a newstable clustering algorithmNatural-SDK-means++(NSDK-means++)in a novel way.First,to complete the evaluation dimensions of the existing IVIs,we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating the connectivity with other two commonly considered measures of compactness and separation.Then,considering the stability,number of parameters and clustering performance,we proposed the NSDK-means++to participate in the integrationwhere the natural neighbor was used to optimize the initial cluster centers(ICCs)determination strategy in the SDK-means++.At last,to ensure the objectivity of the estimatedONS,we designed a BWCON-based ONS estimation framework that does not require the user to set any parameters in advance and integrated the NSDK-means++into this framework forming a practical ONS estimation tool BWCON-NSDK-means++.The final experimental results showthat the proposed BWCONand NSDK-means++are significantlymore suitable than their respective existing models to participate in the integration for determining theONS,and the proposed BWCON-NSDK-means++is demonstrably superior to the BWCON-KMA,BWCONMBK,BWCON-KM++,BWCON-RKM++,BWCON-SDKM++,BWCON-Single linkage,BWCON-Complete linkage,BWCON-Average linkage and BWCON-Ward linkage in terms of the ONS estimation.Moreover,as an independentmarket segmentation tool,the BWCON-NSDK-means++also outperforms the existing models with respect to the inter-market differentiation and sub-market size.
基金Sponsored by the "Twelfth Five-year" National Science and Technology Supoort Programe(Grant No.2011BAJ05B02-03)
文摘In the study on functional low-carbon ergonomic validity in buildings,ergonomic validity is different from resource validity which is easy for quantitative analysis. To eliminate the complexity and uncertainty impacts of human factors on quantitative study,it proposes a method of building a parameter of ergonomic validity—multi-effect time by using cardiotachometer to record heart rate change,being used to evaluate the functional low-carbon ergonomic validity targeting at the ontological characteristics of kitchen. This method is used to determine the physical consumption intensity( multi-effect) through heart rate incremental relation based on the principles of physiology and ergonomics,and to confirm the ergonomic validity of environmental factors by the time to complete standard work as well as multi-effect quantitative analysis. The test results show that,under the kitchen operating conditions,the multi-effect( ME) can properly reflect the real-time status of the operator and is easily operated; the parameters obtained are not significantly related to the physiological status of the operator,and multi-effect time( MT) is sensitive to the physical consumption brought about to the operator due to kitchen environmental factors; thus,it can be taken as an objective index,which is simple and easy to operate in residential kitchen functional low-carbon evaluation.
文摘This study aimed to examine the surface and content validity of the Mentoring Function Scale for Novice Nurses, used to assess the mentoring of entry-level nurses, and to refine the scale items. In Study 1, six nurse education researchers, selected using convenience sampling, with five or more years of nursing experience and experience teaching novice nurses, were invited to an expert meeting in July 2015. A group interview was conducted that lasted approximately 120 minutes. Study 2 examined the content validity index. Between September and November 2015, we distributed a self-administered questionnaire survey to 11 participants selected by convenience sampling. The participants included five nurse education researchers with a minimum of five years of nursing experience and experience teaching novice nurses, as well as six clinical nurses with a master’s degree or higher. Finally, 81 questionnaire items were retained from the initial 125 items. The 81-item Mentoring Function Scale for Novice Nurses had higher content validity than the original scale. To further increase the scale’s applicability, future studies should assess its reliability, construct validity, and criterion-related validity.
基金Supported by the National Natural Science Foundation of China(61139002)~~
文摘Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.
基金supported by the Research Management Center,Xiamen University Malaysia under XMUM Research Program Cycle 4(Grant No:XMUMRF/2019-C4/IECE/0012).
文摘Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.
基金This research was funded by the ESF Project No.8.2.2.0/20/I/003“Strengthening of Professional Competence of Daugavpils University Academic Personnel of Strategic Specialization Branches 3rd Call”,Nr.14-85/14-2022/10.
文摘Given the everlasting significance of knowledge in society and academia,this article proposes a theoretical and methodological perspective on conceptualizing and investigating it.Specifically,it aims to explore the epistemological attitude(EA)theory and its semantic approach to assessing sources of knowledge.The article provides a concise overview of the EA theory,which advocates for a systemic perspective on cognition and knowledge.It introduces and elaborates on the core concept and model,which serve as the foundation for the proposed methodology.This methodology suggests examining knowledge objects through subjective,contextual,and epistemological realms as multi-level knowledge constructs.Emphasizing the importance of semantics in studying knowledge,categories,and meanings,the article proposes an epistemological attitude towards sources of knowledge semantic questionnaire.The article delves into the methodology,reflecting on its four consecutive stages.It begins with the formal and substantive stages,which involve selecting sources,choosing academic experts as target participants,and developing content.The procedural stage follows,in which an expert review approach is employed to assess the content validity of the method.Finally,the article discusses the semantic method,elucidating its structure,features,semantic categories,and assessment procedure.The proposed method provides a unique contribution by enabling the analysis of the epistemological and socio-psychological meanings of sources,representing them as semantic constructs.
基金the National Natural Science Foundation of China (60234030)the Natural Science Foundationof He’nan Educational Committee of China (2007520019, 2008B520015)Doctoral Foundation of Henan Polytechnic Universityof China (B050901, B2008-61)
文摘Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm （WFCA） instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.
基金supported by the Open Project of Xiangjiang Laboratory(No.22XJ02003)the National Natural Science Foundation of China(No.62122093).
文摘Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,which may not capture all the features of the data.This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance measure.Therefore,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decision-makers.Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality.The paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.
基金supported by the National Natural Science Foundation of China(Grant No.51176030)Jiangsu Science and Technology Department(Grant No.BY2015070-17)
文摘The furnace process is very important in boiler operation,and furnace pressure works as an important parameter in furnace process.Therefore,there is a need to analyze and monitor the pressure signal in furnace.However,little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions.Since pressure sequence contains complex information,it demands feature extraction methods from multi-aspect consideration.In this paper,fuzzy c-means analysis method based on weighted validity index(VFCM)has been proposed for the working condition classification based on feature extraction.To deal with the fluctuating and time-varying pressure sequence,feature extraction is taken as nonlinear analysis based on entropy theory.Three kinds of entropy values,extracted from pressure sequence in time-frequency domain,are studied as the clustering objects for work condition classification.Weighted validity index,taking the close and separation degree into consideration,is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number.Each time FCM runs,the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value.Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM.Pressure sequences got from a 300 MW boiler are then taken for case study.The result of the pressure sequence case study with an error rate of 0.5332%shows the valuable information on boiler’s load and pressure sequence in furnace.The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed.Moreover,the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.
