Improving the Grid-based Clustering by Identifying Cluster Center Nodes and Boundary Nodes Adaptively

Yaru Li, Yue Xi, Yonggang Lu

Abstract: Clustering analysis is a data analysis technology, which divides data objects into different clusters according to the similarity between them. The density-based clustering methods can identify clusters with arbitrary shapes, but its time complexity can be very high with the increasing of the number and the dimension of the data points. The grid-based clustering methods are usually used to deal with the problem. However, the performance of these grid-based methods is often affected by the identification of the cluster center and boundary based on global thresholds. Therefore, in this paper, an adaptive grid-based clustering method is proposed, in which the definition of cluster center nodes and boundary nodes is based on relative density values between data points, without using a global threshold. First, the new definitions of the cluster center nodes and boundary nodes are given, and then the clustering results are obtained by an initial clustering process and a merging process of