This paper provides a general formulation of probabilistic model-based clustering with deterministic annealing (DA), which leads to a unifying analysis of k-means, EM clustering, soft competitive learning algorithms (e.g., self-organizing map), and information bottleneck. The analysis points out an interesting yet not well-recognized connection between the k-means and EM clustering—they are jus...

This paper is about the evaluation of the results of clustering algorithms, and the comparison of such algorithms. We propose a new method based on the enrichment of a set of independent labeled datasets by the results of clustering, and the use of a supervised method to evaluate the interest of adding such new information to the datasets. We thus adapt the cascade generalization [1] paradigm i...

Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a meaningful set of common functional features. Part I of this paper reviews the following issues related to clustering approaches found in the literature: relative (probabili...

Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a meaningful set of common functional features. Part I of this paper reviews the following issues related to clustering approaches found in the literature: relative (probabili...

Text classification, the grouping of texts into several clusters, has been used as a means of improving both the efficiency and the effectiveDess of text retrieval/categorization In this paper we propose a hierarchical clustering algor i thm that constructs a Bet of clusters having the maximum Bayesian posterior probability, the probability that the given texts are classified into clusters We c...