Data clustering using the bees algorithm
Duc Truong Pham, Sameh Otri, Ali Afify, Massudi Mahmuddin, H. Al-Jabbouli · Universiti Utara Malaysia Institutional Repository (Universiti Utara Malaysia) · 2007
Clustering is concerned with partitioning a data set into homogeneous groups. One of the most popular clustering methods is k-means clustering because of its simplicity and computational efficiency. K-means clustering involves search and optimization. The main problem with this clustering method is its tendency to converge to local optima. The authors’ team have developed a new population based search algorithm called the Bees Algorithm that is capable of locating near optimal solutions efficiently. This paper proposes a clustering method that integrates the simplicity of the k-means algorithm with the capability of the Bees Algorithm to avoid local optima. The paper presents test results to demonstrate the efficacy of the proposed algorithm.