An efficient clustering method for high-dimensional data mining

Most clustering methods for data mining applications do not work efficiently when dealing with large, high-dimensional data. This is caused by socalled 'curse of dimensionality' and the limitation of available memory. In this paper, we propose an efficient clustering method for handling of large amounts of high-dimensional data. Our clustering method provides both an efficient cell creation and a cell insertion algorithm. To achieve good retrieval performance on clusters, we also propose a filtering-based index structure using an approximation technique. We compare the performance of our clustering method with the CLIQUE method. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.