标题：An Improved Possibilistic Fuzzy Entropy Clustering Based on Artificial Bee Colony Algorithm
作者：Guo, Baofeng; Jiang, Mingyan
作者机构：[Guo, Baofeng; Jiang, Mingyan] Shandong Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.
会议名称：2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE)
会议日期：NOV 20-21, 2016
来源：PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRIAL ENGINEERING (AIIE 2016)
关键词：possibilistic fuzzy clustering; partition entropy; unsupervised; possibilistic clustering; artificial bee colony algorithm
摘要：In this paper, a possibilistic fuzzy entropy clustering algorithm (PFECM) based on unsupervised possibilistic clustering (UPC) algorithm and partition entropy (PE) has been proposed. Meanwhile, an efficient global optimization method-artificial bee colony (ABC) algorithm is introduced to optimize the proposed model. ABC-PFECM has two significant advantages compared with other algorithm. Firstly, it inherited the merits of PFCM including strong robust to noise and exclusion of the consistent clustering. Secondly, ABC-PFECM could eliminate the defects that PFCM is sensitive to the initial value and easily fall into the local optimal solution. Experimental results show splendid performance of our algorithm in decreasing computational complexity, improving clustering accuracy and enhancing global optimization capabilities.