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.KeywordsIndex StructureRetrieval TimeData Mining ApplicationSpatial Data MiningCell ThresholdThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.