OPTIMIZATION OF FUZZY CLUSTERING CRITERIA BY A HYBRID PSO AND FUZZY C-MEANS CLUSTERING ALGORITHM
نویسندگان
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
3 DEPARTMENT OF INDUSTRIAL ENGINEERING, SCIENCE & RESEARCH BRANCH, ISLAMIC AZAD UNIVERSITY, TEHRAN, IRAN
doi
10.22111/ijfs.2008.339چکیده
This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find agood or near-optimal solution in reasonable time, but we show that its performancemay be improved by seeding the initial swarm with the result of the c-meansalgorithm. Various clustering simulations are experimentally compared with the FCMalgorithm in order to illustrate the efficiency and ability of the proposed algorithms.
