Balanced seed selection for K-means clustering with determinantal point process

Abstract

K-means is one of the most popular and effective partitional clustering algorithms. However, in K-means, the initial seeds (centroids) play a critical role in determining the quality of the clusters. The existing methods address this problem either by factoring in the distance between the points on n-dimensional space so that the seeds are spaced apart or by choosing points from the dense regions to avoid the selection of outliers. We introduce a novel approach for seed selection that jointly models diversity as well as the quality of the seeds in a unified probabilistic framework based on a fixed-size determinantal point process. The quality indicator measures the reliability of the point to be considered as a potential seed, while the diversity measure factors in the spatial relation between the points on Euclidean space. The results show that the proposed algorithm outperforms the state-of-the-art models on several datasets.