Selecting the best measures to discover quantitative association rules

Abstract

The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be included in the fitness function in order to obtain better values for the whole set of quality measures, and not only for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to the fitness function of any algorithm. To validate if better results are obtained when using the function fitness composed of the subset of measures proposed here, the existing QARGA algorithm has been applied to a wide variety of datasets. Finally, a comparative analysis of the results obtained by means of the application of QARGA with the original fitness function is provided, showing a remarkable improvement when the new one is used.

Improving a multi-objective evolutionary algorithm to discover quantitative association rules

“…In a previous work [24], the authors proposed a methodology to automatically select a subset of measures whose optimization leads to the optimization of the entire set of measures. This work was focused on finding relations among different quality measures in order to determine which measures must be included in the fitness function.…”

“…3 has been applied to select the best subset of measures to be optimized by MOQAR, according to the study carried out in [24]. Following with that study, QARGA [23] has been applied to 32 datasets from the BUFA repository described in Sect.…”

“…Therefore, the study proposed in [24] is used as first step to discover the most adequate objectives to be included in the fitness function in order to simultaneously optimize as many measures as possible.…”

A Sensitivity Analysis for Quality Measures of Quantitative Association Rules

“…Several datasets have been tested from the public BUFA repository [6]. In particular, the thirty-five public datasets from BUFA repository used in [9]. Note that Buying, Country, College, Education, Read and Usnews Colleged have been preprocessed using K-means Imputation method proposed in [5] (available in the KEEL tool [4]) in order to deal with missing values.…”

“…Therefore, the main goal of this work is to conduct an analysis on the sensitivity of such quality measures when the weights in the fitness function vary. Nonetheless, there also exist other measures widely used for both evaluation and optimization of AR [9]. Some of such quality measures are described in Table 1.…”