A Grid-based Improving Clustering Quality Algorithm

In order to improve the quality of grid-based clustering,the paper presents a technique of distinguish between outliers and boundary points of clusters,which uses distance from point of a sparse cell to the center of the dense cell as criterion function,and develops HQGC algorithm using this technique.The experimental results show that it can discover arbitrary shapes of clusters,the accuracy of clustering results of HQGC is high,with the merit of only requiring one data scan,HQGC is efficient with its run time being linear to the size of the input data set,and scale well.