A Model for Visualizing Critical Least Association Rules

Spring Conference on Computer Graphics - SCCG '13, 2013

: The quantity of involvement of the data in the rules is emphasized by the opacity (left) and on the Z axis (right).

Lecture Notes in Computer Science, 2004

Data mining is increasingly becoming important in extracting interesting information from large databases. Many industries are using data mining tools for analyzing their vast databases and making business decisions. Mining association rules is an important data mining method where interesting associations or correlations are inferred from large databases. Though there are many algorithms for mining association rules, these algorithms have some shortcomings. Most of these algorithms usually find a large number of association rules and many of these rules are not interesting in practice. Hence, there is a need for human intervention in mining interesting association rules. Moreover, such intervention is most effective if the human analyst has a robust visualization tool for mining and visualizing association rules. In this paper we present a three-step visualization method for mining market basket association rules. These steps include discovering frequent itemsets, mining association rules and finally visualizing the mined association rules. Most previous visualization methods have concentrated only on visualizing association rules that have been already mined by using existing algorithms. Our method allows an analyst complete control in mining meaningful association rules through visualization of the mining process.

In order to help the expert to validate association rules, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data structure from which the rules are extracted. The second one has two subcategories: The first one consists on providing to the expert a tool for rule interactive exploration. In fact, they present these rules in textual form. The second subcategory includes the use of visualization systems to facilitate the task of rules mining. However, this last subcategory assumes that experts have statistical knowledge to interpret and validate association rules. Furthermore, the statistical methods have a lack of semantic representation and could not help the experts during the process of validation. To solve this problem, we propose in this paper a method which visualizes to the experts a synthetic representation of association rules as a formal conceptual graph (FCG). FCG represents his area of interest and allows him to realize the task of rules mining easily due to its semantic richness.

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].

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.

2002

Abstract Effective visualization is an important aspect of active data mining. In the context of association rules, this need has been driven by the large amount of rules produced from a run of the algorithm. To be able to address real user needs, the rules need to be summarized and organized so that it can be interpreted and applied in a timely manner. In this paper, we propose two visualization techniques that is an improvement over those used by existing data mining packages.

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.

Visual Informatics: Bridging Research and …, 2009

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.

Lecture Notes in Computer Science, 2012

Association rules are a very popular non-supervised data mining technique for extracting co-relation in large set of data transactions. Although the vast use, the analysis of mined rules may be intricate for non-experts, and the technique effectiveness is constrained by the data dimensionality. This paper presents a pre-processing approach that uses (1) dual scaling in order to present the mined rules with some semantic contextualization that assists interpretation, and (2) mean shift clustering to reduce data dimensionality. We tested our model with real data collected from accident reports in petroleum industry.