Relative Density Clustering Algorithm Based on Density Fluctuation

Abstract

With strong ability of noise resistance and discovery clusters of arbitrary shapes makes, the traditional density-based clustering algorithm has been widely used in clustering analysis. However, the quality of clustering results would be reduced significantly when the traditional density clustering algorithm was used to analyze uneven density datasets. Though the classical OPTICS algorithm can identify the clusters with uneven density, it is useless to the clusters whose boundaries are adjacent or mixed. To overcome this problem, a density fluctuation-based relative density clustering algorithm, named DFRDC (Relative Density Clustering Algorithm Based on Density Fluctuation) is proposed. The experimental results show that DFRDC can effectively identify multiple clusters with different densities and outperform of the state-of-the-art density clustering algorithms.