Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

Yan, Xiaowei; Zhang, Chengqi; Zhang, Shichao

doi:10.1016/j.eswa.2008.01.028

Cited by 182 publications

(137 citation statements)

References 9 publications

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“…If the number of attributes and their values is very large, it leads to a large search space and very big output rule set. The proposed EARMGA algorithm can mine high quality rules without the user specifying minimum support or confidence threshold levels (Yan et al, 2009).…”

Section: Jcsmentioning

confidence: 99%

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

Mangat

Vig

2014

Journal of Computer Science

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Evolutionary algorithms are capable of finding near optimal solutions to problems which are intractable to solve using conventional methods. One such problem is to accurately classify patients using rule mining methodology while controlling the size of output rules. A massive amount of data pertaining to medicine is generated and recorded daily. Uncovering useful knowledge and assisting decision makers in the diagnosis and treatment of diseases from this vast data has become imperative. Association rule mining is an obvious choice for representing this previously hidden information as rules are simple to understand and infer. These rules can be used to understand the etiology of diseases and classify patients based on recorded characteristics. The interestingness of such an algorithm for rule mining will be determined by its accuracy and ability to produce easily understandable rules. This study applies latest improvements in swarm intelligence to devise a novel strategy for rule mining that exhibits high predictive accuracy and comprehensibility. It has been applied over four medical datasets to classify patients as fit or unfit. The paper begins with an explanation of rule mining functionality and concept of swarm intelligence. The current techniques for rule mining in the medical domain are surveyed and their shortcomings are identified. This is followed by a description of the proposed algorithm which includes a novel rule discovery procedure and a novel rule list selection criterion. The results of the proposed algorithm thus obtained, are compared with the other best known approaches. Finally, the future scope of work in this area is briefly discussed.

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Section: Jcsmentioning

confidence: 99%

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

Mangat

Vig

2014

Journal of Computer Science

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“…in particular, to deal with data mining-based problems is a hot topic of research nowadays (Alcalá-Fdez et al 2009aChen et al 2010;del Jesús et al 2009;Yan et al 2009). Also, EA have been used to build rule-based systems (Aguilar-Ruiz et al 2007;Berlanga et al 2010;Orriols-Puig and Bernadó-Mansilla 2009).…”

Section: Quality Parametersmentioning

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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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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“…For discovering association rules, the value of support plays important role. in work [4] present a new scheme based on a genetic algorithm for finding different association rules without specifying minimum support value. in literature, many techniques, either based on genetic algorithm [10,11] or based on other optimization algorithm have been presented to enhanced association rules from a huge dataset [6,7].…”

Section: Introductionmentioning

confidence: 99%

Improved Association Rules Optimization using Modified ABC Algorithm

Bhadoriya¹,

Dutta²

2015

IJCA

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To discover the frequent item sets from the huge data sets, one of the most popular techniques of data mining, called association rule mining technique used. For generating association rules from huge database using association rule mining technique, Computer system takes too much. This can be enhanced, if the number of association rules generated using association rule mining technique from a huge dataset can be optimized. So here in this work, firstly association rules are generated using standard Apriori algorithm and then optimized these association rules using modified artificial bee colony (ABC) algorithm. In this modified ABC algorithm, one additional operator, called crossover operator, is used after the third phase, called scout bee phase, of ABC algorithm. Due to the better exploration property of crossover operator, it is used in this work. Experimental results show that the proposed schemes performance better than previously proposed schemes like K-Nearest Neighbor algorithm (KNN) and ABC algorithm.

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Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

Cited by 182 publications

References 9 publications

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

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

Improved Association Rules Optimization using Modified ABC Algorithm

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