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clustering validity function Fusion Method of FCM clustering Algorithm Based on Dempster-Shafer Evidence Theory

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第1期24卷 650-675页

作者： Wang, Hong-Yu Wang, Jie-Sheng Wang, Guan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

With the deepening of the research on clustering algorithm, clustering validity has become an indispensable part of cluster analysis. However, due to the complexity of data structure and different attributes, any clustering validity function cannot be applied to all datasets, so clustering validity function has been bringing forth new ones. Therefore, this paper proposes a clustering validity function fusion model based on D-S evidence theory (DS-CVFFM), which adopts FCM clustering algorithm as the base algorithm, calculates the values of different validity functions, and then uses the values of different clustering validity functions as the evidence source to construct the basic probability assignment function (BPA). Finally, it integrates with the fusion rules of D-S evidence theory, and outputs the optimal clustering number according to the decision conditions. DS-CVFFM uses the information fusion of multiple clustering validity functions to judge the number of optimal clusters without the need to propose complex validity functions, and avoid the influence of expert factors in the weighted combination clustering validity evaluation method. Finally, 4 sets of artificial datasets and 14 sets of UCI datasets are selected to verify the effectiveness of the proposed model. The experimental results show that compared with the traditional clustering validity evaluation methods, the proposed fusion model has a significant improvement in the accuracy of judging the optimal number of clusters, and the stability is improved under different values of fuzzy exponent, which can overcome the shortcomings of traditional clustering validity evaluation methods.

关键词： D-S evidence theory FCM clustering algorithm clustering validity function Information fusion

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Fuzzy C-Means clustering validity function Based on Multiple clustering Performance Evaluation Components

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第4期24卷 1859-1887页

作者： Wang, Guan Wang, Jie-Sheng Wang, Hong-Yu Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

clustering is the process of grouping a set of physical or abstract objects into multiple similar objects. Fuzzy C-means (FCM) clustering is one of the most widely used clustering methods, whose main research goal is to find the optimal clustering number of data sets, which is related to whether the data can be effectively divided. The study of clustering validity function is the process of evaluating the clustering quality and determining the optimal clustering number. Based on the idea of components, six cluster performance evaluation components are proposed to define compactness, variation, similarity, overlap and separation of data sets, respectively. Then a new validity function based on FCM clustering algorithm is synthesized by these six components. Finally, the proposed validity function and eight typical validity functions are compared on five artificial data sets and eight UCI data sets. The simulation results show that the proposed clustering validity function can evaluate the clustering results more effectively and determine the optimal clustering number of different data sets.

关键词： Fuzzy C-means clustering algorithm clustering validity function clustering performance evaluation components

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Exponent and Logarithm Component-Wise Construction Method of FCM clustering validity function Based on Subjective and Objective Weighting

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2023年第2期25卷 647-669页

作者： Liu, Jia-Xu Wang, Jie-Sheng Wang, Guan Zhao, Xiao-Rui Wang, Hong-Yu Jin, Di Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Inspect & Certificat Ctr Liaoning Anshan 114051 Peoples R China

The cluster validity function is used to evaluate the quality of the cluster results, and giving the exact number of initial cluster categories will rationalize the cluster results. Most single cluster validity functions and combined cluster validity functions generally have strong subjective problems, which also increases the burden on decision analysts and have great limitations in applications. To overcome the shortcomings of these clustering validity functions and improve the accuracy of the optimal cluster category classification for the datasets, based on the clustering performance evaluation components, a validity functional component construction method based on the exponential and log form was proposed. The weighting method adopts the combination of expert empowerment and standard separation method to combine the five weights so as to obtain 52 different fuzzy clustering validity functions. Then, based on the fuzzy C-mean (FCM) clustering algorithm, the performance analysis are carried out by using multiple data sets. Experimental simulation of these functions are proceeded on six commonly used UCI datasets. A clustering validity function with the simplest structure and the best classification effect was selected by comparison. Finally, this function is compared with 8 typical single clustering validity functions and four common clustering validity combination evaluation methods on 8 UCI data sets. Through experimental simulation, the proposed validity function is compared in processing data sets, but also has strong scientific theoretical basis. Thus, the feasibility and effectiveness of the proposed clustering validity function construction method are proved.

关键词： Fuzzy c-means clustering algorithm clustering validity function Subjective and objective weighting Component-wise design

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Ratio component-wise design method of fuzzy c-means clustering validity function

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4691-4707页

作者： Wang, Guan Wang, Jie-Sheng Wang, Hong-Yu Liu, Jia-Xu Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Peoples R China

Fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy C-means (FCM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FCM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering validity functions are formed. Then, these functions are verified experimentally under six kinds of UCI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UCI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets.

关键词： Data mining fuzzy c-means clustering algorithm clustering validity function ratio component-wise design

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Component-wise design method of fuzzy C-means clustering validity function based on CRITIC combination weighting

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JOURNAL OF SUPERCOMPUTING 2023年第13期79卷 14571-14601页

作者： Wang, Guan Wang, Jie-Sheng Wang, Hong-Yu Liu, Jia-Xu Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

clustering validity function is an important research direction in clustering problems. Its idea is to specify the number of data clusters in advance so as to judge the optimal partition result on data sets. Studies have shown that no clustering validity function can handle all types of data, and its performance is not consistently better than other indices. Therefore, a component-wise design method of the fuzzy C-means (FCM) clustering validity function based on CRITIC combination weighting is proposed by adopting components for evaluating clustering performance. The weighting method combines expert weighting and the coefficient of variation method (CRITIC), arranges and combines six validity components with weights, and generates 55 different fuzzy clustering validity functions. These clustering validity functions are then tested on six typical UCI data sets, and the function with the simplest structure and best classification performance is selected through comparison. Finally, it is compared with seven typical clustering validity functions and four common combination clustering validity evaluation methods on eight UCI data sets. The simulation results demonstrate that the proposed validity function can achieve better classification results and determine the optimal cluster number for different data sets.

关键词： Fuzzy C-means clustering algorithm clustering validity function CRITIC combination weighting Component-wise design

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A New clustering validity function for the Fuzzy C-means Algorithm

A New Clustering Validity Function for the Fuzzy C-means Alg...

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20th Chinese Control and Decision Conference

作者： Wang, Jiesheng Liaoning Univ Sci & Technol Sch Elect & Informat Engn Anshan 114051 Peoples R China

ISBN: (纸本)9781424417339

Fuzzy C-means (FCM) clustering algorithm is the unsupervised extraction of groups from an unlabelled data set with no prior knowledge of the underlying data structure. However there is a major limitation that exists in this method. A predefined number of clusters must be given in advance. In this paper, we propose a new validity index to deal with this situation. The performance evaluation of the proposed cluster validity index compares favorably with that of several validity functions and shows the effectiveness.

关键词： Fuzzy Partition Fuzzy C-means clustering clustering validity function

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A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 12411-12432页

作者： Wang, H. Y. Wang, J. S. Zhu, L. F. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness.

关键词： Fuzzy C-means clustering algorithm clustering validity function membership matrix intra-class compactness inter-class separation

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A New validity function For Fuzzy clustering

A New Validity Function For Fuzzy Clustering

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International Conference on Computational Intelligence and Natural Computing

作者： Li, Yang Yu, Fusheng Beijing Normal Univ Sch Math Sci Lab Math & Complex Syst Minist Educ Beijing 100875 Peoples R China

ISBN: (纸本)9780769536453

This paper first gives a new validity function for fuzzy clustering, then presents a method of the optimal selecting of the cluster number in the standard fuzzy c-means clustering algorithm, and finally outlines the fuzzy c-means clustering algorithm with parameters self-adapted. Experimental results carried on synthetic data set and data set based on actual background illustrate the performance of the new validity function and the corresponding fuzzy clustering algorithm.

关键词： Fuzzy C-Means fuzzy clustering analysis cluster number clustering validity function

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A New validity function For Fuzzy clustering

A New Validity Function For Fuzzy Clustering

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2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Yang Li, Fusheng Yu School of Mathematical Sciences, Beijing Normal University Laboratory of Mathematics and Complex Systems, Ministry of Education Beijing 100875, The People's Republic of China

关键词： Fuzzy C-Means fuzzy clustering analysis cluster number clustering validity function

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An effective clustering scheme for high-dimensional data

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期83卷 45001页

作者： He, Xuansen He, Fan Fan, Yueping Jiang, Lingmin Liu, Runzong Maalla, Allam Guangzhou Coll Commerce Sch Informat Technol & Engn Guangzhou Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha Peoples R China Beijing Inst Technol Sch Management & Econ Beijing Peoples R China Guangzhou Coll Technol & Business Sch Engn Guangzhou Peoples R China

While the classical K-means algorithm has been widely used in many fields, it still has some defects. Therefore, this paper proposes a scheme to improve the clustering quality of K-means algorithm. The farthest initial center selection and the min-max rule are used to improve the random initialization of K-means algorithm, which can avoid the empty clusters in the clustering results. For high-dimensional data sets, standardized feature scaling makes the data subject to normal distribution, and supervised linear discriminant analysis (LDA) is used to effectively reduce the data dimension and facilitate visualization. The empirical rule is used to estimate the range of the number of clusters. Within this range, the number of clusters of data is visually estimated by searching the elbow of the sum-of-squared-errors (SSE) curve. Further, a novel clustering validity function f(K) is proposed to determine the optimal number of clusters for complex real-world data sets. Through silhouette analysis, the clustering quality can be intuitively evaluated by calculating the silhouette coefficient of cluster and observing its size. The simulation results of different types of data sets show that this scheme can not only improve the clustering quality of K-means algorithm, but also provide a visual cluster analysis method for high-dimensional data sets.

关键词： K-means algorithm clustering validity function Initial center selection Empirical rule Linear discriminant analysis Silhouette analysis

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