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Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional data sets
University of Borås, Swedish School of Library and Information Science.
2013 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 66, 193-201 p.Article in journal (Refereed)
Abstract [en]

Two-way seriation is a popular technique to analyse groups of similar instances and their features, as well as the connections between the groups themselves. The two-way seriated data may be visualized as a two-dimensional heat map or as a three-dimensional landscape where colour codes or height correspond to the values in the matrix. To achieve a meaningful visualization of high-dimensional data, a compactly supported convolution kernel is introduced, which is similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional space with values that interpolate nearby elements, and provides insight into the clustering structure. Ordinary two-way seriation is also extended to deal with updates of both the row and column spaces. Combined with the convolution kernel, a three-dimensional visualization of dynamics is demonstrated on two data sets, a news collection and a set of microarray measurements.

Place, publisher, year, edition, pages
Elsevier BV , 2013. Vol. 66, 193-201 p.
Keyword [en]
Two-way seriation, Gaussian filtering, High-dimensional data, Hamiltonian path
National Category
Probability Theory and Statistics
Research subject
Library and Information Science; Bussiness and IT
URN: urn:nbn:se:hb:diva-1559DOI: 10.1016/j.csda.2013.03.026ISI: 000321087500016Local ID: 2320/12278OAI: diva2:869617
Available from: 2015-11-13 Created: 2015-11-13

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Wittek, Peter
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