document: Learning spatially variant dissimilarity ( SVaD ) measures Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed ( dis ) similarity measure between the data points in feature space . This makes the type of clusters identified highly dependent on the assumed similarity measure . Building on recent work in this area , we formally define a class of spatially varying dissimilarity measures and propose algorithms to learn the dissimilarity measure automatically from the data . The idea is to identify clusters that are compact with respect to the unknown spatially varying dissimilarity measure . Our experiments show that the proposed algorithms are more stable and achieve better accuracy on various textual data sets when compared with similar algorithms proposed in the literature .
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