A new cell-based clustering method for large, high-dimensional data in data mining applications
Jae‐Woo Chang, Du-Seok Jin · 2002
Recently data mining applications require a large amount of high-dimensional data. However, most clustering methods for data miming do not work efficiently for dealing with large, high-dimensional data because of the so-called 'curse of dimensionality'[1] and the limitation of available memory. In this paper, we propose a new cell-based clustering method which is more efficient for large, high-dimensional data than the existing clustering methods. Our clustering method provides an efficient cell creation algorithm using a space-partitioning technique and uses a filtering-based index structure using an approximation technique. Finally, we compare the performance of our cell-based clustering method with the CLIQUE method in terms of cluster construction time, precision, and retrieval time. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.