A Comparison of Clustering Algorithm Based on Bee Colony

Mohammadjavad Hosseinpoor, Behnam Baghban, Mahnaz Mehdipoor

Abstract


Clustering is one of the important approaches of data mining that can be used for data analysis and is applicable in different fields. Clustering divides those groupings which are more similar in the case of interest parameters. Bee colony algorithm is one of the existing algorithms for clustering. The bee colony algorithm is one of the new stochastic optimization that is inspired by the collective behavior of bees. One problem with this algorithm is late converging and trapping in local optimum. This article compares the bee colony algorithms which are proposed to solve the problem of clustering. The results of comparison show that the performance of algorithms combined with bees contains more accuracy in comparison with their individual performances as well as k-means clustering algorithm.

Keywords


clustering, artificial bee colony algorithm, k-means, IABC algorithm, HABC algorithm.

References


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