title: Choosing the number of clusters in Κ-means clustering.
abstract: Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple clusters, the procedure is extended to determine the number of clusters. Through a series of simulations, the proposed methodology is shown to outperform several other commonly used procedures for determining both the presence of clusters and their number.
authors: Department of Psychological Sciences, University of Missouri, Columbia, MO 65203, USA. steinleyd@missouri.edu
authors: Douglas
authors: D
authors: Steinley
authors: Michael J
authors: MJ
authors: Brusco
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011584:Psychology / Q000379:methods; D015203:Reproducibility of Results