Visualizing association rules with interactive mosaic plots

Hofmann, Heike; Siebes, Arno; Wilhelm, Adalbert F. X.

doi:10.1145/347090.347133

Cited by 105 publications

(71 citation statements)

References 6 publications

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“…Linear diagrams were introduced by Leibniz [3], with parallel bargrams [4] and double decker plots [5] being similar. Each set is represented as one or more horizontal line segments, with all sets drawn in parallel.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Sets: An Empirical Comparison of Diagram Types

Chapman

Rodgers

Micallef

et al. 2014

Diagrammatic Representation and Inference

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Abstract. There are a range of diagram types that can be used to visualize sets. However, there is a significant lack of insight into which is the most effective visualization. To address this knowledge gap, this paper empirically evaluates four diagram types: Venn diagrams, Euler diagrams with shading, Euler diagrams without shading, and the less well-known linear diagrams. By collecting performance data (time to complete tasks and error rate), through crowdsourcing, we establish that linear diagrams outperform the other three diagram types in terms of both task completion time and number of errors. Venn diagrams perform worst from both perspectives. Thus, we provide evidence that linear diagrams are the most effective of these four diagram types for representing sets.

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Section: Introductionmentioning

confidence: 99%

Visualizing Sets: An Empirical Comparison of Diagram Types

Chapman

Rodgers

Micallef

et al. 2014

Diagrammatic Representation and Inference

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show abstract

“…The structure of a decision tree is most often visualized as a simple hierarchy of nodes (see Figure 1), although more elaborate schemes such as mosaic plots [15], icicle plots [16], and the sunburst layout [17] have also been used. Comprehension can be aided by displaying the distribution of instances at each node or by showing an indication of the confidence at each stage of the tree [18].…”

Section: Related Workmentioning

confidence: 99%

Visualization of Decision Tree State for the Classification of Parkinson's Disease

Williams

Mudali

Buddelmeijer

et al. 2016

JBEMi

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Decision trees have been shown to be effective at classifying subjects with Parkinson's disease when provided with features (subject scores) derived from FDG-PET data. Such subject scores have strong discriminative power but are not intuitive to understand. We therefore augment each decision node with thumbnails of the principal component (PC) images from which the subject scores are computed, and also provide labeled scatter plots of the distribution of scores. These plots allow the progress of individual subjects to be traced through the tree and enable the user to focus on complex or unexpected classifications. In addition, we present a visual representation of a typical brain activity pattern arriving at each leaf node, and show how this can be compared to a known reference to validate the behaviour of the tree.

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“…In [19] they focused on the visualization of a subset of association rules generated from the original context. These rules include only items associated with a set of items initially chosen in order to have a better study.…”

Section: Visualization Systemsmentioning

confidence: 99%

“…While the second one is "Infor" Which indicates if visualized generic bases are informative or not. Info [37] Matrix item-to-item L No - [38] Matrix item-to-rule L No - [19] Representation on mosaic rules L No -MIRAGE [40] Galois lattice L Yes No [39] 2D with parallel coordinates L No -ARVis [4] 3D landscape L No -GenVis [2] Histogram 2D ; Histogram3D H Yes Yes [16] 2D Scatter H No - [10] 2D Scatter H No -…”

Section: Visualization Systemsmentioning

confidence: 99%