A Clustering of Interestingness Measures

Vaillant, Benoît; Lenca, Philippe; Lallich, Stéphane

doi:10.1007/978-3-540-30214-8_23

Cited by 54 publications

(26 citation statements)

References 15 publications

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“…PiatetskyShapiro [12] proposes a framework with three properties and we set our work with respect to it. Other works compare interestingness measures to determine their differences and similarities, either in an experimental manner [16] or in a theoretical one [15,7]. There are also attempts to combine several measures to benefit from their joint qualities [6].…”

Section: Covering and Selecting The Most Interesting Rulesmentioning

confidence: 98%

Optimized Rule Mining Through a Unified Framework for Interestingness Measures

Hébert

Crémilleux

2006

Data Warehousing and Knowledge Discovery

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Abstract. The large amount of association rules resulting from a KDD process makes the exploitation of the patterns embedded in the database difficult even impossible. In order to address this problem, various interestingness measures were proposed for selecting the most relevant rules. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conflicting information. In this paper, we propose a unified framework for a set of interestingness measures M and prove that most of the usual objective measures behave in a similar way. In the context of classification rules, we show that each measure of M admits a lower bound on condition that a minimal frequency threshold and a maximal number of exceptions are considered. Furthermore, our framework enables to characterize the whole collection of the rules simultaneously optimizing all the measures of M. We finally provide a method to mine a rule cover of this collection.

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Section: Covering and Selecting The Most Interesting Rulesmentioning

confidence: 98%

Optimized Rule Mining Through a Unified Framework for Interestingness Measures

Hébert

Crémilleux

2006

Data Warehousing and Knowledge Discovery

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“…There are two kinds of measures: the subjective (user-oriented) ones and the objective (data-oriented) ones. Subjective measures take into account the user's goals and domain knowledge [14] [16], whereas only the data cardinalities appear in the calculation of objective measures (surveys can be found in [22], [11], [24], [2]). In this article, we are interested in the objective measures.…”

Section: Introductionmentioning

confidence: 99%

Using Information-Theoretic Measures to Assess Association Rule Interestingness

Blanchard

Guillet

Gras

et al.

Fifth IEEE International Conference on Data Mining (ICDM'05)

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“…Other approaches and techniques were presented by studying the similarity between the measures for classifying them [5] or by proposing a set of criteria to design good interestingness measures [4]. Vaillant et al [6] Propose to extract a pre-order on twenty measures and identify the clusters of measures. Those approaches are not guaranteed the selection of the best and the proper interestingness measures for the simple reason that this measure is not verified the used properties.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Select the Interesting Association Rules with Multiple Criteria

Dahbi¹,

Jabri²,

Balouki³

et al. 2017

IJIES

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Abstract:Using the association rules in datamining is one of the most relevant techniques in modern society, aiming to extract the interesting correlation and relation among sets of items or products in large transactional databases. The huge number of extracted association rules represents the main problem that a decision maker can face. Hence, the knowledge post-processing phase becomes very important and challenging to define the most interesting association rules, many interestingness measures have been proposed. Currently, there is no optimal measure that can be selected to evaluate the extracted association rules. To bypass this problem, we propose an approach based on multi-criteria optimization aiming to find a good compromise without excluding any measures. The experiments performed on numerous benchmark datasets show that the proposed algorithm is properly reducing a large number of association rules and keeping the most significant and interesting ones compared to other approaches which illustrate the efficiency and the applicability our approach.

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A Clustering of Interestingness Measures

Cited by 54 publications

References 15 publications

Optimized Rule Mining Through a Unified Framework for Interestingness Measures

Optimized Rule Mining Through a Unified Framework for Interestingness Measures

Using Information-Theoretic Measures to Assess Association Rule Interestingness

A New Method to Select the Interesting Association Rules with Multiple Criteria

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