Fast and general density peaks clustering
Sami Sieranoja, Pasi Fränti · Pattern Recognition Letters · 2019
Density peaks is a popular clustering algorithm, used for many different applications, especially for non-spherical 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.