Fuzzy Honey Bees Foraging Optimization: Swarm Intelligence Approach for Clustering

Research Article
Fuzzy Honey Bees Foraging Optimization: Swarm Intelligence Approach for Clustering

Ali-Asghar Gholami, Ramin Ayanzadeh and Elaheh Raisi

Journal of Artificial Intelligence, 2014, 7(1), 13-23.

Abstract

Clustering is one of the most important steps in data mining; it is known for its phenomenal functionalities in complex real world applications including biology, basic science, medicine, engineering and social science. In this sense, owing to the remarkable effects of clustering on data mining area, wide varieties of clustering approaches have been introduced to cluster data into significant subsets in order to obtain useful information. In this study, a novel clustering method based on honey bees foraging optimization algorithm and fuzzy rules is proposed. In the proposed method, fine shade of local and global search in honey bees optimization algorithm is schemed to be applied to improve the clustering efficiency. Furthermore, fuzzy operators are employed to enhance the performance of new proposed approach and prevent premature convergence. To verify and validate the functionality proposed of method, new method is run on three known data sets of the UCI Machine Learning Repository. Results of clustering reveal that proposed method estimate more desirable clusters compared to the state of the art clustering methods. Moreover, this method appears very stable in multiple tests.

ASCI-ID: 33-136

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