Iterative multiple component analysis with a Renyi entropy-based dissimilarity measure

In this paper, we study the notion of entropy for a set of attributes of a table and propose a novel method to measure the dissimilarity of categorical data. Experiments show that our estimation method improves the accuracy of the popular unsu- pervised Self Organized Map (SOM), in comparison to Euclidean or Mahalanobis distance. The distance comparison is applied for clustering of multidimensional contingency tables. Two factors make our distance function attractive: first, the general framework which can be extended to other class of problems; second, we may normalize this measure in order to obtain a coefficient similar for instance to the Pearson's coefficient of contingency