A Sensitivity Analysis for Quality Measures of Quantitative Association Rules

Martínez-Ballesteros, M.; Martínez‐Álvarez, Francisco; Troncoso, Alicia; Riquelme, José C.

doi:10.1007/978-3-642-40846-5_58

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…All the measures considered in this work as well as their mathematical formulation are summarized in Table 1, where n(X) is the number of occurrences of the itemset X in the dataset and N is the total number of instances in the dataset. ND A preliminary study about how the weights, in a fitness function based on a sum of weighted measures, have an influence on the quality measures was provided in [20]. However, this study was reduced as it was applied only for the weights associated to the support and confidence measures.…”

Section: Introductionmentioning

confidence: 99%

Obtaining optimal quality measures for quantitative association rules

et al. 2016

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a b s t r a c tThere exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of the weights of the measures included in the fitness function. Therefore, user's decision is very important in order to specify the weights of the measures involved in the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the weights for confidence, support, amplitude and number of attributes measures included in the fitness function have an influence on different quality measures according to several minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered depending on the group of measures to be optimized. Finally, the results obtained when using the recommended weights for two real-world applications related to ozone and earthquakes are reported.

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Section: Introductionmentioning

confidence: 99%

Obtaining optimal quality measures for quantitative association rules

et al. 2016

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Attribute Level Clustering Approach to Quantitative Association Rule Mining

PhaniKrishnaKishore¹,

Madamsetti²

2014

IJCA

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Generating rules from quantitative data has been widely studied ever since Agarwal and Srikanth explored the problem through their works on association rule mining. Discretization of the ranges of the attributes has been one of the challenging tasks in quantitative association rule mining that guides the rules generated. Also several algorithms are being proposed for fast identification of frequent item sets from large data sets.In this paper a new data driven partitioning algorithm has been proposed to discretize the ranges of the attributes. Also a new approach has been presented to create meta data for the given data set from which frequent item sets can be generated quickly for any given support counts.

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A Sensitivity Analysis for Quality Measures of Quantitative Association Rules

Cited by 2 publications

References 13 publications

Obtaining optimal quality measures for quantitative association rules

Obtaining optimal quality measures for quantitative association rules

Attribute Level Clustering Approach to Quantitative Association Rule Mining

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