[01] Manij Ranjbar, Computer department, University of Kurdistan, Sanandaj, Iran. [02] Mostafa Azami, Computer department, University of Kurdistan, Sanandaj, Iran. [03] Ali Shokouhi Rostammi, Electrical and Computer faculty, Mazandran University of Science and Technology, Behshahr, Iran.

In this paper, "fuzzy artificial bee algorithm (FABC)" has been proposed for clustering data, this method is an algorithm derived from honeybees to find food in the global and local search to find the best centres in clusters. This algorithm in comparison with other well-known modern heuristic algorithms such as ABC, GA, TS, SA, ACO, K-means, FCM, and PSO improved significantly that fuzzy ABC algorithm had the best performance among other algorithms for the best, average and worst inter-cluster distances. Experiments on Iris and Wine data sets show that the new method is better. On the other hand, we checked Fuzzy ABC algorithm by two well-known functions "Gaussian and Cauchy", that among Fuzzy algorithm and exclusive Honey Bee algorithm, Fuzzy algorithm has better improvement and among Gaussian function and Cauchy function, Gaussian function has better improvement, too.

Fuzzy, Honey Bee, Colony, Clustering

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