Neural Computation, Vol 11, Issue 2 521-540, Copyright © 1999 by The MIT Press

LETTERS

An On-line agglomerative clustering method for nonstationary data

ID Guedalia, M London and M Werman
Institute of Computer Science, Hebrew University, Jerusalem 91904, Israel.idavidg@pacbell.net

An on-line agglomerative clustering algorithm for nonstationary data is described. Three issues are addressed. The first regards the temporal aspects of the data. The clustering of stationary data by the proposed algorithm is comparable to the other popular algorithms tested (batch and on-line). The second issue addressed is the number of clusters required to represent the data. The algorithm provides an efficient framework to determine the natural number of clusters given the scale of the problem. Finally, the proposed algorithm implicitly minimizes the local distortion, a measure that takes into account clusters with relatively small mass. In contrast, most existing on-line clustering methods assume stationarity of the data. When used to cluster nonstationary data, these methods fail to generate a good representation. Moreover, most current algorithms are computationally intensive when determining the correct number of clusters. These algorithms tend to neglect clusters of small mass due to their minimization of the global distortion (Energy).

This article has been cited by other articles:

				Z. Li, J. Han, J. Sun, Y. He, and F. Chu Fault Recognition Method Based on Independent Component Analysis and Hidden Markov Model Journal of Vibration and Control, February 1, 2007; 13(2): 125 - 137. [Abstract] [PDF]