Clustering by comparing regions of different density

Abstract

This paper presents a histogram-based clustering method that automatically determines the number of clusters in a set of data points. Input data are first partitioned into several rectangular blocks. The number of points in each block is determined, and the thirty percent of the blocks with the most points are marked to obtain a feature. Next, the forty percent of the blocks with the most points are marked to obtain a second feature. These two features are then compared to determine the number of clusters in the input data. The proposed clustering method is fast, and the data to be clustered do not need to be linearly separable. Experimental results are included.