The Seeding Algorithm for Spherical k-Means Clustering with Penalties

摘要

Spherical &-means clustering is a generalization of k-means problem which is NP-hard and has widely applications in data mining. It aims to partition a collection of given data with unit length into k sets so as to minimize the within-cluster sum of cosine dissimilarity. In this paper, we introduce the spherical k-means clustering with penalties and give a 2 max{2, M}(1 + M)(ln k + 2)-approximate algorithm, where M is the ratio of the maximal and the minimal penalty values of the given data set.