DCF: An Efficient and Robust Density-Based Clustering Method
Joshua W.D. Tobin, Mimi Zhang
Abstract
Density-based clustering methods have been shown to achieve promising results in modern data mining applications. A recent approach, Density Peaks Clustering (DPC), detects modes as points with high density and large distance to points of higher density, and hence often fails to detect low-density clusters in the data. Furthermore, DPC has quadratic complexity. We here develop a new clustering algorithm, aiming at improving the applicability and efficiency of the peak-finding technique. The improvements are threefold: (1) the new algorithm is applicable to large datasets; (2) the algorithm is capable of detecting clusters of varying density; (3) the algorithm is competent at deciding the correct number of clusters, even when the number of clusters is very high. The clustering performance of the algorithm is greatly enhanced by directing the peak-finding technique to discover modal sets, rather than point modes. We present a theoretical analysis of our approach and experimental results to verify that our algorithm works well in practice. We demonstrate a potential application of our work for unsupervised face recognition.