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Visual matrix explorer for collaborative seriation

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Abstract 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 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization

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

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

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Repertory grid visualized in multiple permutations.

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An example repertory grid.

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

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Workspace with two highlighted patterns.

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An overview of the VME workspace.

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A dataset rendered in different permutations.

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Workflow from acquiring predicate data to visual knowledge mining.

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

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Two sites of insectivores and rodents in Germany and France.

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

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

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Findings of mammal species by location, a subset.

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Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization
Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis

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