h-plots for displaying nonmetric dissimilarity matrices

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H-plots for Displaying Nonmetric Dissimilarity Matrices

Non-metric pairwise data with violations of symmetry, reflexivity or triangle inequality appear in fields such as image matching, web mining or cognitive psychology. When data are inherently non-metric, we should not enforce metricity as real information could be lost. The multidimensional scaling problem is addressed from a new perspective. I propose a method based on the h-plot, which natural...

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of “ H - plots for displaying non - metric dissimilarity matrices . ”

In this file, I show more results and examples from the paper “H-plots for displaying non-metric dissimilarity matrices”, together with more illustrative results on other databases: an image database of handwritten “3”s, the classical Iris data set, and the Swiss roll dataset, considered in Tenenbaum et al. [16] and Roweis and Saul [12]. Each database corresponds to a different section. This wo...

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Dissimilarity Plots:

For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with ...

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Dissimilarity Plots: A Visual Exploration Tool for Partitional Clustering

For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with ...

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Partitioning hard clustering algorithms based on multiple dissimilarity matrices

This paper introduces hard clustering algorithms that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. These matrices have been generated using different sets of variables and dissimilarity functions. These methods are designed to furnish a partition and a prototype for each cluster as well as to learn a rel...

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ژورنال

عنوان ژورنال: Statistical Analysis and Data Mining: The ASA Data Science Journal

سال: 2013

ISSN: 1932-1864

DOI: 10.1002/sam.11177