Optimized Association Rule Mining with genetic algorithms

Wakabi-Waiswa, Peter P.; Baryamureeba, Venansius; Sarukesi, K.

doi:10.1109/icnc.2011.6022183

Cited by 26 publications

(17 citation statements)

References 12 publications

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“…Wakabi-Waiswa, P.P., et al, proposed [12] "Generalized Association Rule Mining Using Genetic Algorithms". In this paper, Association rule mining is designed for the Genetic Algorithms(GAM) at various levels of minsupport.…”

Section: Genetic Algorithm Used In the Association Rulesmentioning

confidence: 99%

An Analytical Study of Genetic Algorithm for Generating Frequent Itemset and Framing Association Rules At Various Support Levels

Kumar¹,

usha²

2016

IOSR

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Section: Genetic Algorithm Used In the Association Rulesmentioning

confidence: 99%

An Analytical Study of Genetic Algorithm for Generating Frequent Itemset and Framing Association Rules At Various Support Levels

Kumar¹,

usha²

2016

IOSR

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“…It was shown that employing non−random initialization can reach to an elevation in the quality of the solution and can drastically decrease the runtime [24]. We, therefore, devised an initialization method which involves picking a training instance to serve as a "seed" for rule generation based on the alteration of itemsets within each level [31].…”

Section: Generic Operatorsmentioning

confidence: 99%

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

Darwish¹,

Amer²,

Taktak³

2016

ijarai

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Abstract-Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the userspecified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.

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“…If the number of conditions involved in the antecedent part is less than the number of conditions in the consequent part, the rule is more comprehensible [44]. The comprehensibility is computed by (5).…”

Section: Pareto Based Approachmentioning

confidence: 99%

MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Rule Discovery

Kahvazadeh¹,

Abadeh²

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Optimized Association Rule Mining with genetic algorithms

Cited by 26 publications

References 12 publications

An Analytical Study of Genetic Algorithm for Generating Frequent Itemset and Framing Association Rules At Various Support Levels

An Analytical Study of Genetic Algorithm for Generating Frequent Itemset and Framing Association Rules At Various Support Levels

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Rule Discovery

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