Visual Comparison of Association Rules

Hofmann, Heike; Wilhelm, Adalbert F. X.

doi:10.1007/s001800100075

Cited by 27 publications

(11 citation statements)

References 5 publications

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“…Values on intersections represent the strength of rules (support or confidence). Hofmann in his modification [9] used to express the support and confidence of a rule colours and tones. The two-dimensional makes it possible to visualize only matrix only of one-to-one associations.…”

Section: Related Workmentioning

confidence: 99%

Approach to visualisation of evolving association rule models

Hlosta

Šebek

Zendulka

2013

2013 Second International Conference on Informatics &Amp; Applications (ICIA)

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Visualization of evolving data mining models can allow good insight for a data analyst about these models and their changes in time. This paper presents our approach to visualization of evolving association rule models. This approach is based on graph visualization where nodes of the graph represent itemsets, and edges represent association rules. We show how evolving models, produced by data mining algorithms, are stored in the knowledge base, and then how they can be filtered and visualized. Two ways of graph based visualization of evolving models are shown using force based layout algorithms -local and global layout. Experiments with both are illustrated with emphasis on the latter.

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Section: Related Workmentioning

confidence: 99%

Approach to visualisation of evolving association rule models

Hlosta

Šebek

Zendulka

2013

2013 Second International Conference on Informatics &Amp; Applications (ICIA)

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“…An association graph can quickly turn into a tangled display with as few rules. Mosaic plot was introduced by [7] to visualize contingency tables and have been arranged for association rules in [29]. Each rule is represented by a rectangle, whose width is the support and height is the confidence.…”

Section: Related Workmentioning

confidence: 99%

Contribution to the Association Rules Visualization for Decision Support: A Combined Use Between Boolean Modeling and the Colored 2D Matrix

Benhacine¹,

Atmani²,

Abdelouhab³

2019

IJIMAI

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In the present paper we aim to study the visual decision support based on Cellular machine CASI (Cellular Automata for Symbolic Induction). The purpose is to improve the visualization of large sets of association rules, in order to perform Clinical decision support system and decrease doctors' cognitive charge. One of the major problems in processing association rules is the exponential growth of generated rules volume which impacts doctor's adaptation. In order to clarify it, many approaches meant to represent this set of association rules under visual context have been suggested. In this article we suggest to use jointly the CASI cellular machine and the colored 2D matrices to improve the visualization of association rules. Our approach has been divided into four important phases: (1) Data preparation, (2) Extracting association rules, (3) Boolean modeling of the rules base (4) 2D visualization colored by Boolean inferences.

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“…In (Hao et al, 2001), the method is implemented in 3D with a self-organization algorithm which optimizes the graph layout in space. A second rule visualization method consists in using a matrix representation (Hofmann and Wilhelm, 2001) (Han et al, 1997). In an item-to-item matrix, each line corresponds to an antecedent item and each column to a consequent item.…”

Section: Related Workmentioning

confidence: 99%

A 2D–3D visualization support for human-centered rule mining

Blanchard

Pinaud

Kuntz

et al. 2007

Computers & Graphics

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On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge, the user needs to rummage through the rules. To make this task easier, we propose a new interactive mining methodology based on well-adapted dynamic visual representations. It allows the user to drive the discovery process by focusing his/her attention on limited subsets of rules. We have implemented our methodology with two complementary 2D and 3D visualization supports. These implementations exploit the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.

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Visual Comparison of Association Rules

Cited by 27 publications

References 5 publications

Approach to visualisation of evolving association rule models

Approach to visualisation of evolving association rule models

Contribution to the Association Rules Visualization for Decision Support: A Combined Use Between Boolean Modeling and the Colored 2D Matrix

A 2D–3D visualization support for human-centered rule mining

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