The relations between two distance matrices on the same nite set are analyzed via metric scaling by correlating principal axis Some applications are given and illustrated with examples Introduction Dissimilarities similarities and distances are fundamental concepts in mul tidimensional scaling and related topics Euclidean and Mahalanobis dis tance also play a basic role in techniques such as re...

There is a very famous joke about Bose’s work in Giridh. Professor Mahalanobis wanted Bose to visit the paddy fields and advise him on sampling problems for the estimation of yield of paddy. Bose did not very much like the idea, and he used to spend most of the time at home working on combinatorial problems using Galois fields. The workers of the ISI used to make a joke about this. Whenever Pro...

We discuss an approach to change detection in digital remotely sensed imagery that relies on the Fuzzy Post Classification Comparison technique. We use the fuzzy -means classifier together with the Mahalanobis distance as the basis for a metric of class membership for a individual pixel. We note that the value of the fuzzy exponent in a fuzzy classifier is based on the ratios of the reciprocals...

The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the c...

We use the forward search to provide robust Mahalanobis distances to detect the presence of outliers in a sample of multivariate normal data. Theoretical results on order statistics and on estimation in truncated samples provide the distribution of our test statistic. We also introduce several new robust distances with associated distributional results. Comparisons of our procedure with tests u...

We present two results which arise from a model-based approach to hierarchical agglomerative clustering. First, we show formally that the common heuristic agglomerative clustering algorithms – Ward’s method, single-link, complete-link, and a variant of group-average – are each equivalent to a hierarchical model-based method. This interpretation gives a theoretical explanation of the empirical b...