We use a redundant wavelet transform analysis to detect clusters in high-dimensional data spaces. We overcome Bellman's \curse of dimensionality" in such problems by (i) using some canonical ordering of observation and variable (document and term) dimensions in our data, (ii) applying a wavelet transform to such canonically ordered data, (iii) modeling the noise in wavelet space, (iv) dening signicant component parts of the data as opposed to insignicant or noisy component parts, and (v) reading o the resultant clusters. The overall complexity of this innovative approach is linear in the data dimensionality. We describe a number of examples and test cases, including the clustering of high-dimensional hypertext data. 1 Introduction Bellman's (1961) [1] \curse of dimensionality" refers to the exponential growth of hypervolume as a function of dimensionality. All problems become tougher as the dimensionality increases. Nowhere is this more evident than in problems related to ...

: Multidimensionnal scaling ( MDS ) seeks to build points in a metric space from a given proximity data. MDS analyses the proximity data in a way that displays the structure of the distance-like data as a geometrical picture. In this paper, we study the multidimensional scaling algorithm based on individual differences scaling and present two new ideas for transforming scales of dissimilarities. On the other hand and mainly, we evaluate a new family of dissimilarities based on a probabilistic approach through three data sets, and compare final configurations to the the results obtained with other types of dissimilarities. Key-words: Multidimentionnal scaling ( MDS ), individual differences scaling, probabilistic approach, transforming. (Resume : tsvp) Centre National de la Recherche Scientifique Institut National de Recherche en Informatique (URA 227) Universit e de Rennes 1 -- Insa de Rennes et en Automatique -- unit e de recherche de Rennes An Evalution of a New Dissimilarities Fam...

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