Cluster Validity Measure Based on Voronoi Diagram

Abstract

Clustering is the data mining method that does not use supervised data. Several clustering methods, such as hard c-means, require the number of clusters in advance. These methods need criterion which estimates the number of clusters such as cluster validity measures. Cluster validity measures are quantitative evaluation criteria based on the geometric shape of cluster partition. Although, Many cluster validity measures have been proposed, cluster validity measures based on the Voronoi diagram that is geometric shape related to clustering is not discussed. In this paper, we proposed cluster validity measures based on Voronoi diagrams. Through experiments, we confirmed that the proposed method can evaluate adequately the artificial data that cannot be evaluated adequately using Xie-Beni’s index.