Fast and general density peaks clustering

Density peaks is a popular clustering algorithm, used for many different applications, especially for nonspherical data. Although powerful, its use is limited by quadratic time complexity, which makes it slow for large datasets. In this work, we propose a fast density peaks algorithm that solves the time complexity problem. The proposed algorithm uses a fast and generic construction of approximate k-nearest neighbor graph both for density and for delta calculation. This approach maintains the generality of density peaks, which allows using it for all types of data, as long as a distance function is provided. For a dataset of size 100,000, our approach achieves a 91:1 speedup factor. The algorithm scales up for datasets up to 1 million in size, which could not be solved by the original algorithm at all. With the proposed method, time complexity is no longer a limiting factor of the density peaks clustering. (C) 2019 The Authors. Published by Elsevier B.V.