Analysis of Measures of Quantitative Association Rules

Martínez-Ballesteros, M.; Riquelme, José C.

doi:10.1007/978-3-642-21222-2_39

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Most often, it su ces to concentrate on the integration of support, con dence, and either elevation or in uence to measure the \quality" of a rule quantitatively. However, the real value of a rule is subjective in terms of e ectiveness and functionality and is strongly dependent on the speci c area and business purposes [60].…”

Section: Fitness Functionmentioning

confidence: 99%

A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Moslehi

Haeri

2019

Scientia Iranica

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Discovering association rules is a useful and common technique for data mining, in which relations and co-dependencies of datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and con dence criteria. In this paper, a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the genetic algorithm, and there is no need to determine the extent of the threshold for the support and con dence criteria. By proposing a multi-criteria method, useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discretize numerical values and determine the minimum support threshold and minimum con dence threshold. Di erent criteria are considered to determine appropriate rules. In this algorithm, selected rules are extracted based on con dence, interestingness, and cosine 2 . The results obtained from real-world datasets demonstrate the e ectiveness of the proposed approach. The algorithm is used to examine three datasets, and the results show the superior performance of the proposed algorithm compared to similar algorithms.

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Section: Fitness Functionmentioning

confidence: 99%

A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Moslehi

Haeri

2019

Scientia Iranica

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“…The GarNet algorithm was executed modifying the minimum QAR confidence threshold obtained and second, optimizing different group of measures following the study proposed in [27,42] . Each minimum confidence threshold combined with each group of measures comprise an experiment.…”

Section: Second Phase -Qar Mining Processmentioning

confidence: 99%

Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources

et al. 2017

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“…QARGA is a real-coded genetic algorithm designed to discover existing relationships, specifically QAR, among several variables. A detailed description of the algorithm can be found in [10].…”

Section: Description Of Qargamentioning

confidence: 99%

A Sensitivity Analysis for Quality Measures of Quantitative Association Rules

Martínez-Ballesteros

Martínez‐Álvarez

Troncoso

et al. 2013

Lecture Notes in Computer Science

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Abstract. There exist several fitness function proposals based on a combination of weighted objectives to optimize the discovery of association rules. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of such weights. Therefore, in such proposals it is very important the user's decision in order to specify the weights or coefficients of the optimized objectives. Thus, this work presents an analysis on the sensitivity of several quality measures when the weights included in the fitness function of the existing QARGA algorithm are modified. Finally, a comparative analysis of the results obtained according to the weights setup is provided.

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Analysis of Measures of Quantitative Association Rules

Cited by 8 publications

References 15 publications

A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

A Genetic Algorithm based framework for mining quantitative association rules without specifying minimum support and minimum confidence

Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources

A Sensitivity Analysis for Quality Measures of Quantitative Association Rules

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