What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm.

ABSTRACT: The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet

SUBMITTER: Raykov YP 

PROVIDER: S-EPMC5036949 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature