An Improved Possibilistic Fuzzy Entropy Clustering Based on Artificial Bee Colony Algorithm

DOI

10.2991/aiie-16.2016.19How to use a DOI?

Keywords

possibilistic fuzzy clustering; partition entropy; unsupervised possibilistic clustering; artificial bee colony algorithm

Abstract

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Baofeng Guo
AU  - Mingyan Jiang
PY  - 2016/11
DA  - 2016/11
TI  - An Improved Possibilistic Fuzzy Entropy Clustering Based on Artificial Bee Colony Algorithm
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 80
EP  - 83
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.19
DO  - 10.2991/aiie-16.2016.19
ID  - Guo2016/11
ER  -