GDCLU: A New Grid-Density Based ClustrIng Algorithm

Abstract

This paper addresses the density based clustering problem in data mining where clusters are established based on density of regions. The most well-known algorithm proposed in this area is DBSCAN [1] which employs two parameters influencing the shape of resulted clusters. Therefore, one of the major weaknesses of this algorithm is lack of ability to handle clusters in multi-density environments. In this paper, a new density based grid clustering algorithm, GDCLU, is proposed which uses a new definition for dense regions. It determines dense grids based on densities of their neighbors. This new definition enables GDCLU to handle different shaped clusters in multi-density environments. Also this algorithm benefits from scale independency feature. The time complexity of the algorithm is O(n) in which n is number of points in dataset. Several examples are presented showing promising improvement in performance over other basic algorithms like optics in multi-density environments.