1. ¹Korea Small Business Institute(KOSBI), Seoul, Korea
2. ²Chonbuk National University, Jeonju, Jeonbuck, South Korea
3. ³Iowa State University, Ames, IA, USA

Correspondence: J Yang, Department of Industrial and Information Systems Engineering, Chonbuk National University, 418 Engineering Bldg #6, Duckjin-Dong Duckjin-Gu, Jeonju, Jeonbuck 561 756, South Korea. E-mail: jkyang@chonbuk.ac.uk

Received September 2007; Accepted December 2008; Published online 8 April 2009.

Abstract

Scalability of clustering algorithms is a critical issue facing the data mining community. One method to handle this issue is to use only a subset of all instances. This paper develops an optimization-based approach to the partitional clustering problem using an algorithm specifically designed for noisy performance, which is a problem that arises when using a subset of instances. Numerical results show that computation time can be dramatically reduced by using a partial set of instances without sacrificing solution quality. In addition, these results are more persuasive as the size of the problem is larger.