Robust clustering using a kNN mode seeking ensemble

Jonas Nordhaug Myhre, Karl Øyvind Mikalsen, Sigurd Løkse, Robert Jenssen

Abstract: Highlights • A new clustering ensemble based on kNN mode seeking is proposed. • The algorithm is robust with respect to hyperparameters–no manual tuning needed. • The algorithm is faster than the state-of-the art and able to handle high-dimensional data sets. Abstract In this paper we present a new algorithm for parameter-free clustering by mode seeking. Mode seeking, especially in the form of the mean shift algorithm, is a widely used strategy for clustering data, but at the same time prone to poor performance if the parameters are not chosen correctly. We propose to form a clustering ensemble consisting of repeated and bootstrapped runs of the recent kNN mode seeking algorithm, an algorithm which is faster than ordinary mean shift and more suited for high dimensional data. This creates a robust mode seeking clustering algorithm with respect to the choice of parameters and high dimensional input spaces, while at the same inheriting all other strengths of mode seeking in general. We demonstrate promising results on a number of synthetic and real data sets.