A meta-learning approach for determining the number of clusters with consideration of nearest neighbors

An important and challenging problem in data clustering is the determination of the best number of clusters. A variety of estimation methods has been proposed over the years to address this problem. Most of these methods depend on several nontrivial assumptions about the data structure; and such methods may thus fail to discover the true clusters in a dataset that does not satisfy those assumptions. We develop a new approach that takes as a starting point the simple and intuitive observation that close objects should fall within the same cluster, whereas distant ones should not. Based on this simple notion we utilize a new measurement of good clustering called disconnectivity as well as existing goodness measurements; and we embed these measures into a meta-learning approach for estimating the number of clusters. A simulation experiment based on 13 representative models and an application to real world datasets are conducted to show the effectiveness of the proposed method. (C) 2013 Elsevier Inc. All rights reserved.