High-dimensional clustering method for high performance data mining

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.