FastDEC: Clustering by Fast Dominance Estimation

Abstract

k-Nearest Neighbors (k-NN) graph is essential for the various graph mining tasks. In this work, we study the density-based clustering on the k-NN graph and propose FastDEC, a clustering framework by fast dominance estimation. The nearest density higher (NDH) relation and dominance-component (DC), more specifically their integration with the k-NN graph, are formally defined and theoretically analyzed. FastDEC includes two extensions to satisfy different clustering scenarios: FastDEC D for partitioning data into clusters with arbitrary shapes, and FastDEC K for K-Way partition. Firstly, a set of DCs is detected as the results of FastDEC D by segmenting the given k-NN graph. Then, the K-Way partition is generated by selecting the top-K DCs in terms of the inter-dominance (ID) as the seeds, and assigning the remaining DCs to their nearest dominators. FastDEC can be viewed as a much faster, more robust, and k-NN based variant of the classical density-based clustering algorithm: Density Peak Clustering (DPC). DPC estimates the significance of data points from the density and geometric distance factors, while FastDEC innovatively uses the global rank of the dominator as an additional factor in the significance estimation. FastDEC naturally holds several critical characteristics: (1) excellent clustering performance; (2) easy to interpret and implement; (3) efficiency and robustness. Experiments on both the artificial and real datasets demonstrate that FastDEC outperforms the state-of-the-art density methods including DPC.