OPTIMIZATION OF FUZZY CLUSTERING CRITERIA BY A HYBRID PSO AND FUZZY C-MEANS CLUSTERING ALGORITHM

Document Type: Research Paper

Authors

1 DEPARTMENT OF INDUSTRIAL ENGINEERING, SCIENCE & RESEARCH BRANCH, ISLAMIC AZAD UNIVERSITY, TEHRAN, IRAN

2 DEPARTMENT OF INDUSTRIAL ENGINEERING, COLLEGE OF ENGINEERING, UNIVERSITY OF TEHRAN, TEHRAN, IRAN

Abstract

This paper presents an efficient hybrid method, namely fuzzy particle
swarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzy
clustering problem, especially for large sizes. When the problem becomes large, the
FCM algorithm may result in uneven distribution of data, making it difficult to find
an optimal solution in reasonable amount of time. The PSO algorithm does find a
good or near-optimal solution in reasonable time, but we show that its performance
may be improved by seeding the initial swarm with the result of the c-means
algorithm. Various clustering simulations are experimentally compared with the FCM
algorithm in order to illustrate the efficiency and ability of the proposed algorithms.

Keywords


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