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Please use this identifier to cite or link to this item: http://hdl.handle.net/2320/12278

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Title: Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional data sets
Authors: Wittek, Peter
Department: University of Borås. Swedish School of Library and Information Science
Issue Date: 2013
Journal Title: Computational Statistics & Data Analysis
ISSN: 1872-7352
Volume: 66
Pages: 193-201
Publisher: Elsevier BV
Media type: text
Publication type: article, peer reviewed scientific
Keywords: Two-way seriation
Gaussian filtering
High-dimensional data
Hamiltonian path
Subject Category: Subject categories::Mathematics::Computational Mathematics::Probability Theory and Statistics
Strategic Research Area: Library and information science
Business and IT
Abstract: 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.
DOI: 10.1016/j.csda.2013.03.026
URI: http://hdl.handle.net/2320/12278
Sustainable development: -
Appears in Collections:Artiklar och rapporter / Articles and reports (BHS)

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