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A Hybrid Clustering Method Based on Improved Artificial Bee Colony and Fuzzy C-Means Algorithm

Ajit Kumar, Dharmender Kumar, S.K. Jarial


Data clustering is an important data mining technique to create groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct. Fuzzy c-means (FCM) algorithm is a popular data clustering method that works according to the fuzzy membership between data points and cluster centers. However, it has possibilities of convergence to local minima. Artificial Bee Colony (ABC) algorithm is a swarm based algorithm inspired by intelligent foraging behavior of honey bees. In order to make use of merits of both algorithms, a hybrid algorithm (IABCFCM) based on improved ABC and FCM algorithms is proposed in this paper. The IABCFCM algorithm helps the FCM clustering escape from local optima and provides better experimental results on the well known data sets.


Artificial bee colony, F-measure, fuzzy c-means, objective function value, tournament selection.

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