High-Dimensional Clustering Method for High Performance Data Mining

Authors:
Jae-Woo Chang;Hyun-Jo Lee
Affiliations:
Research Center of Industrial Technology, Dept. of Computer Engineering, Chonbuk National University, Chonju, Chonbuk, 561-756, South Korea;Research Center of Industrial Technology, Dept. of Computer Engineering, Chonbuk National University, Chonju, Chonbuk, 561-756, South Korea
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Abstract

Many clustering methods are not suitable as high-dimensional ones because of the so-called `curse of dimensionality' and the limitation of available memory. In this paper, we propose a new high-dimensional clustering method for the high performance data mining. The proposed high-dimensional clustering method provides efficient cell creation and cell insertion algorithms using a space-partitioning technique, as well as makes use of a filtering-based index structure using an approximation technique. In addition, we compare the performance of our high-dimensional clustering method with the CLIQUE method which is well known as an efficient clustering method for highdimensional data. The experimental results show that our high-dimensional clustering method achieves better performance on cluster construction time and retrieval time than the CLIQUE.