Mining Numeric Association Rules with Genetic Algorithms

Mata, Jacinto; Álvarez, José Luis Domínguez; Santos, J.R.

doi:10.1007/978-3-7091-6230-9_65

Cited by 60 publications

(35 citation statements)

References 4 publications

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“…The approach has been initially tested on several widely studied datasets from the public BUFA repository, and the accuracy of QARGA has been compared with that of the algorithms introduced in Yan et al (2009) and Mata et al (2001) of time series are analyzed: synthetically generated and real-world multidimensional temporal data. Sect.…”

Section: Resultsmentioning

confidence: 99%

“…To carry out the experimentation and make a comparison with QARGA, the evolutionary algorithms EARMGA (Yan et al 2009) and GENAR (Mata et al 2001), available in the KEEL tool (Alcalá-Fdez et al 2009b), have been chosen. Table 6 shows the results obtained by EARMGA, GENAR and QARGA for every dataset.…”

Section: Results In Public Datasetsmentioning

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%

“…In particular, EARMGA (Yan et al 2009), GAR (Mata et al 2002) and GENAR (Mata et al 2001) were applied to two real-world datasets, showing their efficiency in terms of coverage and confidence.…”

Section: Related Workmentioning

confidence: 99%

“…This algorithm was executed five times and the average values of such executions were presented. The main parameters of GENAR algorithm (Mata et al 2001) are 100 for the number of the rules, 100 for the size of population and 100 for the number of generations. GENAR use 0.0 for the minimum support and minimum confidence; 0.25 for the probability of selection; 0.7 for the probability of crossover and 0.1 for the probability of mutation; 0.7 for the penalization factor and 2 for the amplitude factor.…”

Section: Configuration For Public Datasetsmentioning

confidence: 99%

See 4 more Smart Citations

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: Resultsmentioning

confidence: 99%

Section: Results In Public Datasetsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Configuration For Public Datasetsmentioning

confidence: 99%

See 3 more Smart Citations

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

Martínez-Ballesteros¹,

Martínez‐Álvarez²,

Troncoso³

et al. 2011

Soft Comput

Self Cite

View full text Add to dashboard Cite

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Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Altay

Alataş

2021

Concurrency and Computation

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In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability. The chaos numbers seem to efficiently represent a set or range of values with lower and upper bounds for variables. In this article, a new chaos-enhanced representation scheme that is based on the notion of chaos numbers is proposed for evolutionary optimization methods. As a first application of chaos-based new encoding type, it is integrated into the novel hybrid intelligent optimization method proposed that is adapted as numerical association rules miner for the first time. The proposed hybrid method can also be used for complex types of search and optimization problems.The method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds the accurate and comprehensible rules automatically. Based on the chaotic encoding, the proposed method easily and effectively adjusts the intervals of the attributes and automatically mines the rules without any preprocess. The performance of the proposed method was tested in real quantitative data sets and results were compared with the other association rule mining methods.According to the obtained results, the proposed method seems promising with respect to different metrics.

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A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

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Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Mining Numeric Association Rules with Genetic Algorithms

Cited by 60 publications

References 4 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

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

A survey on association rules mining using heuristics

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