Novel Interestingness Measures for Mining Significant Association Rules from Imbalanced Data

Abdellatif, Safa; Hassine, Mohamed Ali Ben; Yahia, Sadok Ben

doi:10.1007/978-3-030-15035-8_16

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Lift [11] refers to the ratio between the confidence c of the rule and the subsequent parts of the rule. It represents the interrelationship between the antecedents and the subsequent parts of a rule.…”

Section: Common Interestingness Measures 21 Lift Measurementioning

confidence: 99%

A novel interestingness measure based on fusion model for association rules mining

Yang

2021

MATEC Web Conf.

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Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

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Section: Common Interestingness Measures 21 Lift Measurementioning

confidence: 99%

A novel interestingness measure based on fusion model for association rules mining

Yang

2021

MATEC Web Conf.

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show abstract

“…First of all, with an aim of finding a fitness function for our final metaheuristic we performed a comparison of several interestingness measures (IM). In order to limit the number of rules discovered and preserve the interpretability of the results, many authors suggest using an IM to rank and select the best laws [18][19][20]. In addition to support and confidence, a large number of IM have been created, each one trying to filter out rules that are not predictive enough, repetitive, or otherwise uninteresting, either generally or for a specific application.…”

Section: Research Planmentioning

confidence: 99%

Extracting Interesting Sequence Association Rules from a High-Dimensional Healthcare Dataset using Multiobjective Metaheuristics

Berteloot

2021

Proceedings of the Canadian Conference on Artificial Intelligence

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Performance Analysis of Predictive Association Rule Classifiers using Healthcare Datasets

Nandhini

Rajalakshmi²,

Sivanandam³

2020

IETE Technical Review

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Novel Interestingness Measures for Mining Significant Association Rules from Imbalanced Data

Cited by 4 publications

References 4 publications

A novel interestingness measure based on fusion model for association rules mining

A novel interestingness measure based on fusion model for association rules mining

Extracting Interesting Sequence Association Rules from a High-Dimensional Healthcare Dataset using Multiobjective Metaheuristics

Performance Analysis of Predictive Association Rule Classifiers using Healthcare Datasets

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