α-Approximation Density-based Clustering of Multi-valued Objects

Abstract

Multi-valued data are commonly found in many real applications. During the process of clustering multi-valued data, most existing methods use sampling or aggregation mechanisms that cannot reflect the real distribution of objects and their instances and thus fail to obtain high-quality clusters. In this paper, a concept of -approximation distance is introduced to measure the connectivity between multi-valued objects by taking account of the distribution of the instances. An -approximation density-based clustering algorithm (DBCMO) is proposed to efficiently cluster the multi-valued objects by using global and local R* tree structures. To speed up the algorithm, four pruning rules on the tree structures are implemented. Empirical studies on synthetic and real datasets demonstrate that DBCMO can efficiently and effectively discover the multi-valued object clusters. A comparison with two existing methods further shows that DBCMO can better handle a continuous decrease in the cluster density and detect clusters of varying density.

Tasks

Reproductions