An Efficient Cell-Based Clustering Method for Handling Large, High-Dimensional Data

Abstract

In this paper, we propose an efficient cell-based clustering method for handling a large of amount of high-dimensional data. Our clustering method provides an efficient cell creation algorithm using a space-partitioning technique and a cell insertion algorithm to construct clusters as cells with more density than a given threshold. To achieve good retrieval performance on clusters, we also propose a new filtering-based index structure using an approximation technique. In addition, 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.