A Quantum Swarm Evolutionary Algorithm for mining association rules in large databases
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نویسنده
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Ykhlef Mourad
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منبع
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journal of king saud university - computer and information sciences - 2011 - دوره : 23 - شماره : 1 - صفحه:1 -6
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چکیده
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Association rule mining aims to extract the correlation or causal structure existing between a set of frequent items or attributes in a database. these associations are represented by mean of rules. association rule mining methods provide a robust but non-linear approach to find associations. the search for association rules is an np-complete problem. the complexities mainly arise in exploiting huge number of database transactions and items. in this article we propose a new algorithm to extract the best rules in a reasonable time of execution but without assuring always the optimal solutions. the new derived algorithm is based on quantum swarm evolutionary approach; it gives better results compared to genetic algorithms.
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کلیدواژه
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Quantum EvolutionaryAlgorithm;Swarm intelligence;Association rule mining;Fitness
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آدرس
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King Saud University, College of Computer and Information Sciences, Saudi Arabia
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پست الکترونیکی
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ykhlef@ksu.edu.sa
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