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Novel Clustering Algorithms Based on Improved Artificial Fish Swarm Algorithm

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3 Author(s)
Yongming Cheng ; Sch. of Inf. Sci. & Eng., Shandong Univ., Jinan, China ; Mingyan Jiang ; Dongfeng Yuan

An improved artificial fish swarm algorithm (IAFSA) is proposed, and its complexity is much less than the original algorithm (AFSA) because of a new proposed fish behavior. Based on IAFSA, two novel algorithms for data clustering are presented. One is the improved artificial fish swarm clustering (IAFSC) algorithm, the other is a hybrid fuzzy clustering algorithm that incorporates the fuzzy c-means (FCM) into the IAFSA. The performance of the proposed algorithms is compared with that of the particle swarm optimization (PSO), k-means and FCM respectively on Iris testing data. Simulation results show that the performance of the proposed algorithms is much better than that of the PSO, K-means and FCM. And the proposed hybrid fuzzy clustering algorithm avoids the FCM's weakness such as initialization value problem and local minimum problem.

Published in:

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on  (Volume:3 )

Date of Conference:

14-16 Aug. 2009