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Erschienen in:
2009 | OriginalPaper | Buchkapitel
A Hybrid Bumble Bees Mating Optimization - GRASP Algorithm for Clustering
verfasst von : Yannis Marinakis, Magdalene Marinaki, Nikolaos Matsatsinis
Erschienen in: Hybrid Artificial Intelligence Systems
Verlag: Springer Berlin Heidelberg
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A new hybrid algorithm for clustering, which is based on the concepts of the Bumble Bees Mating Optimization (BBMO) and Greedy Randomized Adaptive Search Procedure (GRASP), is presented in this paper. The proposed algorithm is a two phase algorithm which combines a new algorithm called Bumble Bees Mating Optimization algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. The performance of the algorithm is compared with other popular metaheuristic and nature inspired methods using datasets from the UCI Machine Learning Repository. The high performance of the proposed algorithm is achieved as the algorithm gives very good results and in some instances the percentage of the correct clustered samples is very high and is larger than 98%.