Pham, D.T and Otri, S. and Afify, A. and Mahmuddin, Massudi and Al-Jabbouli, H. (2007) Data clustering using the bees algorithm. In: 40th CIRP International Manufacturing Systems Seminar, May 30- June 1, 2007, Liverpool, UK.
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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.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||data clustering, bees algorithm|
|Subjects:||Q Science > QA Mathematics > QA76 Computer software|
|Divisions:||College of Arts and Sciences|
|Depositing User:||Dr. Massudi Mahmuddin|
|Date Deposited:||06 Jul 2010 07:19|
|Last Modified:||14 Feb 2013 00:48|
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