Development of New Clustering Algorithm Based on Firefly Optimization
Abstract
Clustering is a grouping of data with similar characteristics in a data set. Within the same cluster, the similarities are high and the similarities between the clusters are low. Clustering algorithms often have unsupervised learning, so no prior information is given. In this article, firefly optimization algorithm has been applied to find the optimum cluster centers. This algorithm has a global search capability and generally is used to solve difficult problems. The proposed clustering algorithm was testedon 12 data sets from UCI data warehouse. For evaluation of performance of this new approach, the proposed clustering algorithm are compared with twelve other clustering algorithms (SFLA, ABC, PSO, Bayes Net, Mlp ANN, RBF, KStar, Bagging, Multi Boost, NB Tree, Ridor and VFI). As a result of this study, the proposed approach has performed better than many clustering algorithms in many dataset
Keywords
Clustering , clustering by firefly , clustering methods , firefly optimization.
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