On-line hierarchical clustering

Most of the techniques used in the literature for hierarchical clustering a re based on off-line operation. The main contribution of this paper is to p ropose a new algorithm for on-line hierarchical clustering by finding the n earest k objects to each introduced object so far and these nearest k objec ts are continuously updated by the arrival of a new object. By final object , we have the objects and their nearest k objects which are sorted to produ ce the hierarchical dendogram. The results of the application of the new al gorithm on real and synthetic data and also using simulation experiments, s how that the new technique is quite efficient and, in many respects, superi or to traditional off-line hierarchical methods. (C) 1998 Published by Else vier Science B.V. All rights reserved.