Swarm intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
A genetic fuzzy k-Modes algorithm for clustering categorical data
Expert Systems with Applications: An International Journal
Fuzzy C-Mean Clustering Algorithms Based on Picard Iteration and Particle Swarm Optimization
ETTANDGRS '08 Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing - Volume 02
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This paper presents an efficient hybrid method, namely fuzzy particle swarm optimization (MFPSO) 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. In our work it is shown 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.