Evidential evolving C-means clustering method based on artificial bee colony algorithm with variable strings and interactive evaluation mode

• Published in: Fuzzy optimization and decision making Vol. 20; no. 3; pp. 293 - 313
• Main Authors: Su, Zhi-gang, Zhou, Hong-yu, Hao, Yong-sheng
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Clustering; Evolutionary algorithms; Evolutionary computation; Mathematical Logic and Foundations; Mathematics; Mathematics and Statistics; Operations Research/Decision Theory; Optimization; Optimization algorithms; Probability Theory and Stochastic Processes; Search algorithms; Strings; Swarm intelligence; Upper bounds; Belief functions; Artificial bee colony; Evidential clustering; Soft clustering; Evolutionary computation
• ISSN: 1568-4539; 1573-2908
• DOI: 10.1007/s10700-020-09344-7

The Evidential C-Means algorithm provides a global treatment of ambiguity and uncertainty in memberships when partitioning attribute data, but still requires the number of clusters to be fixed as a priori, like most existing clustering methods do. However, the users usually do not know the exact number of clusters in advance, particularly in practical engineering. To relax this requirement, this paper proposes an Evidential Evolving C-Means (E2CM) clustering method in the framework of evolutionary computation: cluster centers are encoded in a population of variable strings (or particles) to search the optimal number and locations of clusters simultaneously. To perform such joint optimization problem well, an artificial bee colony algorithm with variable strings and interactive evaluation mode is proposed. It will be shown that the E2CM can automatically create appropriate credal partitions by just requiring an upper bound of the cluster number rather than the exact one. More interestingly, there are no restrictions on this upper bound from the theoretic point of view. Some numerical experiments and a practical application in thermal power engineering validate our conclusions.