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Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets

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  • Wittek, Peter

Abstract

Two-way seriation is a popular technique to analyze 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 datasets, a news collection and a set of microarray measurements.

Suggested Citation

  • Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.
    • Handle: RePEc:eee:csdana:v:66:y:2013:i:c:p:193-201
    DOI: 10.1016/j.csda.2013.03.026
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