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WIREs Comp Stat

Visual matrix explorer for collaborative seriation

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In this article, we present a web‐based open source tool to support cross‐disciplinary collaborative seriation with the following goals: to compare different matrix permutations, to discover patterns from the data, annotate it, and accumulate knowledge. Seriation is an unsupervised data mining technique that reorders objects into a sequence along a one‐dimensional continuum to make sense of the whole series. Clustering assigns objects to groups, whereas seriation assigns objects to a position within a sequence. Seriation has been applied to a variety of disciplines including archaeology and anthropology; cartography, graphics, and information visualization; sociology and sociometry; psychology and psychometrics; ecology; biology and bioinformatics; cellular manufacturing; and operations research. Interestingly, across those different disciplines, there are several commonly emerging similar structural patterns. Visual Matrix Explorer allows users to explore and link those patterns, share an online workplace and instantly transmit changes in the system to other users. WIREs Comp Stat 2012, 4:85–97. doi: 10.1002/wics.193

Figure 1.

Workflow from acquiring predicate data to visual knowledge mining.

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Figure 2.

A dataset rendered in different permutations.

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Figure 3.

An overview of the VME workspace.

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Figure 4.

Workspace with two highlighted patterns.

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Figure 5.

Viewing the contents of the pink region—the results of a drilldown.

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Figure 6.

An example repertory grid.

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Figure 7.

Repertory grid visualized in multiple permutations.

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Figure 8.

Two highlighted patterns: magenta for a concept of loveable woman and blue for a rejecting‐threatening person.

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Figure 9.

Violet and plum regions representing a concept of an ideal person.

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Figure 10.

Findings of mammal species by location, a subset.

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Figure 11.

A visualization of the fossils dataset, rows represent sites, columns mark species.

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Figure 12.

Drilldown of the black dense region. Art and zodiac permutations introduce clumps that were not detectable on the original matrix.

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Figure 13.

Two sites of insectivores and rodents in Germany and France.

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Figure 14.

Details of the comet‐tail violet pattern in the initial matrix.

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Data Mining > Clustering and Classification
Data Mining > Exploratory Data Analysis
Data Visualization > Visualization of High Dimensional Data

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