CABGD: An Improved Clustering Algorithm Based on Grid-Density

Abstract

In data mining fields, clustering is an important issue. Compared with other algorithms, grid-based algorithms generally have a fast processing time. However, since the size of a cell is determined by users, the large size cell may contain data points of different clusters and leads to low clustering quality. In this paper, we propose an improved clustering algorithm based on grid-density (CABGD). The concept of center intensity of grid cell is presented and is applied to identify the distribution of data points in a grid and to decide whether or not to split the grid. Then all density-connected grids are assigned to a cluster. Experimental results on synthetic datasets show that the algorithm has higher clustering accuracy and lower sensitivity to parameters.