On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid

Lenca, Philippe; Meyer, Patrick; Vaillant, Benoît; Lallich, Stéphane

doi:10.1016/j.ejor.2006.10.059

Cited by 174 publications

(103 citation statements)

References 27 publications

(24 reference statements)

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…Besides, a user oriented description and multiple criteria decision aid can be incorporated into the recommendation process of one or more user-adapted interestingness measures for association rules (Lenca, Meyer, Vaillant, & Lallich, 2008). A standardization of the environment structure, of the formalisms and of the goals would certainly provide an easier implementation by simplifying preliminary work and would allow other industrial applications.…”

Section: Resultsmentioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

130

View full text Add to dashboard Cite

To cite this version:Bernard Kamsu-Foguem, Fabien Rigal, Félix Mauget. Mining association rules for the quality improvement of the production process. Expert Systems with Applications, Elsevier, 2013, vol. 40, pp. 1034-1045 b s t r a c tAcademics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment's results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.

show abstract

Section: Resultsmentioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

130

View full text Add to dashboard Cite

show abstract

“…Using properties facilitates a general and practical way to automatically identify interesting measures. This trend has been enriched by different other works [2], [5], [11], [12] with an extensive number of properties. Nevertheless, these properties are not standards [10].…”

Section: Introductionmentioning

confidence: 96%

Ranking and Selecting Association Rules Based on Dominance Relationship

Bouker

Saidi

Yahia³

et al. 2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

View full text Add to dashboard Cite

Abstract. The huge number of association rules represent the main obstacle that a decision maker faces. In order to bypass this obstacle, an efficient selection of rules must be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures caused a new problem which is the heterogeneity of the evaluation results and this created confusion to the decision. In this scope, we propose a novel approach to discover interesting association rules without favouring or excluding any measure by adopting the notion of dominance between rules. Our approach bypasses the problem of measure heterogeneity and find a compromise between their evaluations and also bypasses another non-trivial problem which is the threshold value specification.

show abstract

“…To bring even further characterization of measures and to choose a suitable one for a particular application and for particular user's expectations, many properties have been proposed and compared in the literature [9,12,16,26]. In the context of confirmation measures the following properties are often analysed: property M , Ex 1 and weak Ex 1 , L and weak L, and a group of symmetry properties.…”

Section: Confirmation Measures and Their Propertiesmentioning

confidence: 99%

Visualization support for the analysis of properties of interestingness measures

Susmaga¹,

Szczęch²

2015

Bulletin of the Polish Academy of Sciences Technical Sciences

View full text Add to dashboard Cite

Abstract. The paper considers a particular group of rule interestingness measures, called Bayesian confirmation measures, which have become the subject of numerous, but often exclusively theoretical studies. To assist and enhance their analysis in real-life situations, where time constraints may impede conducting such time consuming procedures, a visual technique has been introduced and described in this paper. It starts with an exhaustive and non-redundant set of contingency tables, which consists of all possible tables having the same number of observations. These data, originally 4-dimensional, may, owing to an inherent constraint, be effectively represented as a 3-dimensional tetrahedron, while an additional, scalar function of the data (e.g. a confirmation measure) may be rendered using colour.Dedicated analyses of particular colour patterns on this tetrahedron allow to promptly perceive particular properties of the visualized measures. To illustrate the introduced technique, a set of 12 popular confirmation measures has been selected and visualized. Additionally, a set of 9 popular properties has been chosen and the visual interpretations of the measures in terms of the properties have been presented.

show abstract

On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid

Cited by 174 publications

References 27 publications

Mining association rules for the quality improvement of the production process

Mining association rules for the quality improvement of the production process

Ranking and Selecting Association Rules Based on Dominance Relationship

Visualization support for the analysis of properties of interestingness measures

Contact Info

Product

Resources

About