Cluster validity analysis using subsampling

• Published in: SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483) Vol. 2; pp. 1435 - 1440 vol.2
• Main Authors: Abul, O., Lo, A., Alhajj, R., Polat, F., Barker, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Clustering algorithms; Computer science; Humans; Organizing; Pattern analysis; Sampling methods; Stability analysis; Testing; Visualization
• DOI: 10.1109/ICSMC.2003.1244614
• ISBN: 9780780379527; 0780379527
• ISSN: 1062-922X

Cluster validity investigates whether generated clusters are true clusters or due to chance. This is usually done based on subsampling stability analysis. Related to this problem is estimating true number of clusters in a given dataset. There are a number of methods described in the literature to handle both purposes. In this paper, we propose three methods for estimating confidence in the validity of clustering result. The first method validates clustering result by employing supervised classifiers. The dataset is divided into training and test sets and the accuracy of the classifier is evaluated on the test set. This method computes confidence in the generalization capability of clustering. The second method is based on the fact that if a clustering is valid then each of its subsets should be valid as well. The third method is similar to second method; it takes the dual approach, i.e., each cluster is expected to be stable and compact. Confidence is estimated by repeating the process a number of times on subsamples. Experimental results illustrate effectiveness of the proposed methods.