ACODF: a novel data clustering approach for data mining in large databases

Authors:
Cheng-Fa Tsai;Chun-Wei Tsai;Han-Chang Wu;Tzer Yang
Affiliations:
Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
Venue:
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
Year:
2004

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Abstract

In this paper, we present an efficient clustering approach for large databases. Our simulation results indicate that the proposed novel clustering method (called ant colony optimization with different favor algorithm) performs better than the fast self-organizing map (SOM) combines K-means approach (FSOM+K-means) and genetic K-means algorithm (GKA). In addition, in all the cases we studied, our method produces much smaller errors than both the FSOM+K-means approach and GKA.