Improved Unsupervised Clustering over Watershed-Based Clustering

Abstract

This paper improves upon an existing Watershed algorithm-based clustering method. The existing method uses an experimentally determined parameter to construct a density function. A better method for evaluating the cell/window size (used in the construction of the density function) is proposed, eliminating the need for arbitrary parameters. The algorithm has been tested on both published and unpublished synthetic data, and the results demonstrate that the proposed approach is able to accurately estimate the number of clusters present in the data.