Discovering Numeric Association Rules via Evolutionary Algorithm

Vázquez, Jacinto Mata; Macías, José Luis Álvarez; Santos, José Cristóbal Riquelme

doi:10.1007/3-540-47887-6_5

Cited by 64 publications

(50 citation statements)

References 12 publications

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“…These datasets have been chosen because the literature offers multiple EA applied to them (Alatas and Akin 2006;Mata et al 2002). Later, time series have been synthetically generated to determine the suitability of applying QARGA to temporal data.…”

Section: Quality Parametersmentioning

confidence: 99%

See 1 more Smart Citation

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

Martínez-Ballesteros¹,

Martínez‐Álvarez²,

Troncoso³

et al. 2011

Soft Comput

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An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative association rules. This algorithm, called QARGA (Quantitative Association Rules by Genetic Algorithm), uses a particular codification of the individuals that allows solving two basic problems. First, it does not perform a previous attribute discretization and, second, it is not necessary to set which variables belong to the antecedent or consequent. Therefore, it may discover all underlying dependencies among different variables. To evaluate the proposed algorithm three experiments have been carried out. As initial step, several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches. Also, the algorithm has been applied to synthetic time series (where the relationships are known) to analyze its potential for discovering rules in time series. Finally, a real-world multidimensional time series composed by several climatological variables has been considered. All the results show a remarkable performance of QARGA.

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Section: Quality Parametersmentioning

confidence: 99%

“…Mata et al (2001) proposed a novel technique based on evolutionary techniques to find QAR that was improved in Mata et al (2002). First, the approach found the sets of attributes which were frequently present in database and called frequent itemsets, and later AR were extracted from these sets.…”

Section: Related Workmentioning

confidence: 99%

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

Martínez-Ballesteros¹,

Martínez‐Álvarez²,

Troncoso³

et al. 2011

Soft Comput

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show abstract

“…The first one is optimized association rule mining [9,21,6,17,18,14,23], which contains certain uninstantiated attributes and the mining problem is to determine values for the uninstantiated attributes such that one measure (e.g., support, confidence or gain) is maximized and another measure satisfies a predefined threshold. Inspired by the problem of image segmentation in computer vision, Fukuda et al [9] propose a geometric method to compute the optimized region for association rules.…”

Section: Related Workmentioning

confidence: 99%

“…Mata et al [17,18] use a genetic algorithm to optimize the support of an interval for a quantitative attribute. However, their approach does not guarantee to produce high confidence rules.…”

Section: Related Workmentioning

confidence: 99%

An information-theoretic approach to quantitative association rule mining

Cheng

2007

Knowl Inf Syst

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Abstract. Quantitative Association Rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike Boolean Association Rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. However, the combination of these quantitative attributes and their value intervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency.In this paper, we propose an information-theoretic approach to avoid unrewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normalization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the attributes, we construct a mutual information graph (MI graph), whose edges are attribute pairs that have normalized mutual information no less than a predefined information threshold. We find that the cliques in the MI graph represent a majority of the frequent itemsets. We also show that frequent itemsets that do not form a clique in the MI graph are those whose attributes are not informatively correlated to each other. By utilizing the cliques in the MI graph, we devise an efficient algorithm that significantly reduces the number of value intervals of the attribute sets to be joined during the mining process. Extensive experiments show that our algorithm speeds up the mining process by up to two orders of magnitude. Most importantly, we are able to obtain most of the highconfidence QARs, whereas the QARs that are not returned by MIC are shown to be less interesting.

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“…Genetic Algorithms (GAs) [10] are used to solve AR problems because they offer a set of advantages for knowledge extraction and specifically for rule induction processes. Authors of [14] proposed a genetic algorithm (GA) to discover numeric ARs, dividing the process in two phases. Another GA was used in [17] in order to obtain quantitative ARs and confidence was optimized in the fitness function.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Measures of Quantitative Association Rules

Martínez-Ballesteros

Riquelme

2011

Lecture Notes in Computer Science

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Abstract. This paper presents the analysis of relationships among different interestingness measures of quality of association rules as first step to select the best objectives in order to develop a multi-objective algorithm. For this purpose, the discovering of association rules is based on evolutionary techniques. Specifically, a genetic algorithm has been used in order to mine quantitative association rules and determine the intervals on the attributes without discretizing the data before. The algorithm has been applied in real-word climatological datasets based on Ozone and Earthquake data.

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Discovering Numeric Association Rules via Evolutionary Algorithm

Cited by 64 publications

References 12 publications

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

An evolutionary algorithm to discover quantitative association rules in multidimensional time series

An information-theoretic approach to quantitative association rule mining

Analysis of Measures of Quantitative Association Rules

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