CBCM: A Cell-Based Clustering Method for Data Mining Applications

Abstract

Data mining applications have recently required a large amount of high-dimensional data. However, most clustering methods for the data miming applications do not work efficiently for dealing with large, high-dimensional data because of the so-called ‘curse of dimensionality’ and the limitation of available memory. In this paper, we propose a new cell-based clustering method (CBCM) which is more efficient for large, high-dimensional data than the existing clustering methods. Our CBCM provides an efficient cell creation algorithm using a space-partitioning technique and uses a filtering-based index structure using an approximation technique. In addition, we compare the performance of our CBCM with the CLIQUE method in terms of cluster construction time, precision, and retrieval time.