Intuitionistic fuzzy c-means clustering algorithm based on a novel weighted proximity measure and genetic algorithm
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ORIGINAL ARTICLE
Intuitionistic fuzzy c‑means clustering algorithm based on a novel weighted proximity measure and genetic algorithm Wen‑hui Hou1 · Yi‑ting Wang1 · Jian‑qiang Wang1,2 · Peng‑Fei Cheng2 · Lin Li3 Received: 4 September 2019 / Accepted: 18 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In the era of big data, the research on clustering technologies is a popular topic because they can discover the structure of complex data sets with minimal prior knowledge. Among the existing soft clustering technologies, as an extension of fuzzy c-means (FCM) algorithm, the intuitionistic FCM (IFCM) algorithm has been widely used due to its superiority in reducing the effects of outliers/noise and improving the clustering accuracy. In the existing IFCM algorithm, the measurement of proximity degree between a pair of objects and the determination of parameters are two critical problems, which have considerable effects on the clustering results. Therefore, we propose an improved IFCM clustering technique in this paper. Firstly, a novel weighted proximity measure, which aggregates weighted similarity and correlation measures, is proposed to evaluate not only the closeness degree but also the linear relationship between two objects. Subsequently, genetic algorithms are utilized for identifying the optimal parameters. Lastly, experiments on the proposed IFCM technique are conducted on synthetic and UCI data sets. Comparisons with other approaches in cluster evaluation indexes indicate the effectiveness and superiority of our method. Keywords Intuitionistic fuzzy c-means (IFCM) algorithm · Intuitionistic fuzzification · Similarity measure · Correlation coefficient · Genetic algorithm (GA)
1 Introduction As an extension of fuzzy set (FS) [1], intuitionistic FS (IFS), which consist of membership, non-membership and hesitation degrees, were incepted in [2] to describe and process data with uncertainty. IFS has been continuously studied and applied to various fields, such as pattern recognition, image processing, decision making and clustering [3]. Out of all the applications, the clustering techniques of IFS are among the major domains that have been found to be highly useful but rarely investigated. * Jian‑qiang Wang [email protected] 1
School of Business, Central South University, Changsha 410083, People’s Republic of China
2
Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Hunan University of Science and Technology, Xiangtan 411201, People’s Republic of China
3
School of Business, Hunan University, Changsha 410082, People’s Republic of China
Clustering refers to an exploratory data analysis tool for discovering the data structure in multivariate data sets through association rules and grouping all data into multiple clusters. A good clustering result requires that the items within the same cluster have a maximal degree of association and minimal otherwise. With the arrival of the big data age, many realistic pr
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