DP-Dip: A skinny method for estimating the number and center of clusters

Multi-scales data containing structures at different scales of shape and density is very common in both synthetic and real world. However, it is a big problem to cluster this kind of data accurately. Choosing an appropriate clustering number is the first step, important and not easy. In this paper, we propose a skinny method, DP-Dip, to estimate the number of clusters. Different from many popular methods, DP-Dip does not make any assumptions about data distribution and only admit one assumption: each cluster is a unimodal distribution. Besides, the method never perform complicated formulas and calculations but employ recursion until the final numbers are determined. Specifically, this algorithm firstly finds the density peaks to input the initial numbers, then splits some clusters according to the modality-testing result, finally merge some clusters if they should be combined.