UUM Repository | Universiti Utara Malaysian Institutional Repository
FAQs | Feedback | Search Tips | Sitemap

Data clustering using the bees algorithm

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.

[img] PDF
Restricted to Registered users only

Download (147kB)


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
URI: http://repo.uum.edu.my/id/eprint/153

Actions (login required)

View Item View Item