Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering
Abstract
In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and 3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k needs to be set by the user of the algorithms. (Kang 2013) recently proposed a novel use of determinantal point processes for sampling the initial centroids for the KMEANS algorithm (we call it KMEANSD++). They, however, do not provide any evaluation establishing that KMEANSD++ is better than other algorithms. In this paper, we show that the performance of KMEANSD++ is comparable to KMEANS++ (both of which are better than KMEANSRAND) with KMEANSD++ having an additional that it can automatically approximate the value of k.
Keywords
Cite
@article{arxiv.1410.6975,
title = {Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering},
author = {Apoorv Agarwal and Anna Choromanska and Krzysztof Choromanski},
journal= {arXiv preprint arXiv:1410.6975},
year = {2014}
}