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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15533

Title: A Quantum Swarm Evolutionary Algorithm for Mining Association Rules in Large Database
Authors: Dr. Mourad Ykhlef
Keywords: Quantum Evolutionary Algorithm, Swarm Intelligence, Association rule Mining, Fitness.
Issue Date: 2011
Publisher: Journal of King Saud University, Computer and Information Sciences (2011)
Abstract: 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 a 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 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.
URI: http://hdl.handle.net/123456789/15533
Appears in Collections:College of Computer and Information Sciences

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