An algorithm to mine general association rules from tabular data

Ayubi, Siyamand; Muyeba, Maybin K.; Baraani, Ahmad; Keane, John

doi:10.1016/j.ins.2009.06.021

Cited by 21 publications

(6 citation statements)

References 33 publications

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“…Finally, Ayubi et al [7] proposed an algorithm that mined general rules whose applicability ranged from discrete attributes to quantitative discretized ones. Thus, they stored general itemsets in a tree structure in order for it to be recursively computed.…”

Section: State Of the Artmentioning

confidence: 99%

Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

Martínez-Ballesteros

Troncoso

Martínez‐Álvarez

et al. 2010

ICA

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This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.

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Section: State Of the Artmentioning

confidence: 99%

Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

Martínez-Ballesteros

Troncoso

Martínez‐Álvarez

et al. 2010

ICA

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“…The fitness function maximized both support and confidence of the rule. Finally, some works such as [9] have proposed the use of an extended set of operators to mine general association rules and have evaluated the proposal in terms of confidence and support.…”

Section: Introductionmentioning

confidence: 99%

Selecting the best measures to discover quantitative association rules

et al. 2014

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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.

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“…The problem of frequent itemset mining [1][2][3]7,14,[26][27][28] is discovering the complete set of itemsets that appear with high frequency in transactional databases. However, the utility of the itemsets is not considered in ordinary frequent itemset mining.…”

Section: Introductionmentioning

confidence: 99%