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Introduction and Motivations
Related Work
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GERVIS:” scalable” visualization of generic association rules

Profile image of Sadok Ben YahiaSadok Ben YahiaProfile image of Engelbert Mephu NguifoEngelbert Mephu Nguifo

Abstract

The extremely large number of association rules that can be drawn from -even reasonably sized-datasets has bootstrapped the development of more acute techniques or methods to reduce the size of the reported rule sets. In order to be reliable in a decision making process, such discovered rules have to be both concise and easily understandable for users, and/or as an input to visualization tools [1].

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Design and Evaluation of Visualization Support to Facilitate Association Rules Modeling
A lopes

Association rules mining is a popular data mining modeling tool. It discovers interesting associations or correlation relationships among a large set of data items, showing attribute values that occur frequently together in a given dataset. Despite their great potential benefit, current association rules modeling tools are far from optimal. This article studies how visualization techniques can be applied to facilitate the association rules modeling process, particularly what visualization elements should be incorporated and how they can be displayed. Original designs for visualization of rules, integration of data and rule visualizations, and visualization of rule derivation process for supporting interactive visual association rules modeling are proposed in this research. Experimental results indicated that, compared to an automatic association rules modeling process, the proposed interactive visual association rules modeling can significantly improve the effectiveness of modeling, enhance understanding of the applied algorithm, and bring users greater satisfaction with the task. The proposed integration of data and rule visualizations can significantly facilitate understanding rules compared to their nonintegrated counterpart. FIGURE 1 Proposed conceptual model of visualization support to association rules modeling.

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Exploratory visualization for association rule rummaging
Fabrice Guillet

2003

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge validation is one of the most problematic steps in an association rule discovery process. In order to comprehend this bulk of rules and to find relevant knowledge for decision-making, the user needs to really rummage through the rules. Visualization can be very beneficial to support him/her in this task by improving the intelligibility of the large rule sets and enabling the user to carry out his/her navigation inside them. In this article, we propose to answer the association rule validation problem by designing a visualization for the rule rummaging task. This new approach based on a specific rummaging model relies on interactive rule focusing and on rule quality measures. A first prototype implementing our representation has been developed. It allows the user to appropriate the interesting rules by navigating through a voluminous rule set by trial and error via successive limited subsets.

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A user-driven and quality-oriented visualization for mining association rules
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On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge validation is one of the most problematic steps in an association rule discovery process. In order to find relevant knowledge for decision-making, the user needs to really rummage through the rules. Visualization can be

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Combining visual techniques for association rules exploration
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D. Bruzzese, P. Buono. Combining Visual Techniques for Association Rules Exploration. AVI 2004, 25-28 May, Gallipoli, Italy, pp. 381-384., 2004

The abundance of data available nowadays fosters the need of developing tools and methodologies to help users in extracting significant information. Visual data mining is going in this direction, exploiting data mining algorithms and methodologies together with information visualization techniques. The demand for visual and interactive analysis tools is particularly pressing in the Association Rules context where often the user has to analyze hundreds of rules in order to grasp valuable knowledge. This paper presents a visual strategy to face this drawback by exploiting graph-based technique and parallel coordinates to visualize the results of association rules mining algorithms. The combination of the two approaches allows both to get an overview on the association structure hidden in the data and to deeper investigate inside a specific set of rules selected by the user.

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A New Visualization Metaphor for Association Rules
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In order to discover knowledge from large amount of results generated by the association rules extraction algorithms, visual representations of association rules can be very beneficial to the user. Those representations support the user in finding and validating interesting knowledge. All techniques proposed for association rule visualization have been developed to represent association rule as a hole without paying attention to the relations between attributes and the contribution of each one. In this article, we propose a new visualization metaphor for association rules. This new metaphor represents attributes which make up the antecedent and the consequent, the contribution of each one to the rule, and the correlations between each pair of antecedent and consequent.

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An Interactive Approach to Display Large Sets of Association Rules
J. Rouillard

Lecture Notes in Computer Science

Knowledge Discovery in Databases (KDD) is an active research domain. Due to the number of large databases, various data mining methods were developed. Those tools can generate a large amount of knowledge that needs more advanced tools to be explored. We focus on association rules mining such as "If Antecedent then Conclusion" and more particularly on rules visualization during the post processing stage in order to help expert's analysis. An association rule is mainly calculated depending on two user-specified metrics: support and confidence. All current representations present a common limitation which is effective on small data quantities. We introduced a new interactive approach which combines both a global representation (2D matrix) and a detailed representation (Fisheyes view) in order to display large sets of association rules.

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A scalable association rule visualization towards displaying large amounts of knowledge
Sadok Ben Yahia, Engelbert Mephu Nguifo

2007

Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining (DM). These tools must be able to generate explicit knowledge and to restitute it. Visualization techniques have shown to be an efficient solution to achieve such goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction one. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both DM steps (association rule extraction and visualization) are treated separately in a one way process. Our approach differs, and uses meta-knowledge to guide the user during the mining process. Standing at the crossroads of DM and Human-Computer Interaction (HCI), we present an integrated framework covering both steps of the DM process. Furthermore, our approach can easily integrate previous techniques of association rule visualization.

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Visualizing Association Rules Using Linked Matrix,Graph, and Detail Views
O. Hoeber

International Journal of Intelligence Science, 2013

Although association rule mining is an important pattern recognition and data analysis technique, extracting and finding significant rules from a large collection has always been challenging. The ability of information visualization to enable users to gain an understanding of high dimensional and large-scale data can play a major role in the exploration, identification, and interpretation of association rules. In this paper, we propose a method that provides multiple views of the association rules, linked together through a filtering mechanism. A visual inspection of the entire association rule set is enabled within a matrix view. Items of interest can be selected, resulting in their corresponding association rules being shown in a graph view. At any time, individual rules can be selected in either view, resulting in their information being shown in the detail view. The fundamental premise in this work is that by providing such a visual and interactive representation of the association rules, users will be able to find important rules quickly and easily, even as the number of rules that must be inspected becomes large. A user evaluation was conducted which validates this premise.

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VIE_MGB: A Visual Interactive Exploration of Minimal Generic Basis of Association Rules
wissem bellegha

2005

Mining association rules is an important task, even though the number of rules discovered is often huge. A possible solution to this problem, is to use the Formal Concept Analysis (FCA) mathematical settings to restrict rules extraction to a generic basis of association rules. This one is considered as a reduced set to which we can apply appropriate inference mechanisms to derive redundant rules. In this paper, we introduce a new minimal generic basis MGB of non-redundant association rules based on the augmented Iceberg Galois lattice. The proposed approach involves the inference mechanisms used and a set of experiments applied to several real and synthetic databases. Carried out experiments showed important benefits in terms of reduction in the number of generic rules extracted. We present also a new framework for generating and visually exploring the minimal generic basis MGB.

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Visualization of Fuzzy Association Rules Representing High-Dimensional Problems
Janos Abonyi

11th IPMU International Conference, 2006

Although fuzzy association rule min-ing is a powerful tool to extract potentially useful information from data, most of algorithms generate large number of rules that makes the analysis of the resulted rule base to be difficult. This paper illus-trates that the two-dimensional plot of the fuzzy frequent itemsets can be effectively used to support the ex-ploratory analysis of the extracted rules. To obtain informative plots Sammon mapping is used to express the relative information content of the rules and the related frequent itemsets as distances.

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Related topics

  • Concept analysis
  • Association Rule
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