A mutual information based online evolving clustering approach and its applications

In this article, a new recursive evolving clustering method is proposed based on the well-known Gustafson–Kessel algorithm. The novelty of the proposed method involves the adaptation and integration of the mutual information based formulation to accommodate the Mahalanobis distance, which functions as the similarity measure and the unification of the clustering generation and pruning mechanisms. Example applications of the method are also discussed in the areas of data compression and knowledge extraction.