The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 42-435
Full Name: Mourad Ykhlef
Position: Assistant Professor
Age: ON
Sex: Male
Address: King Saud University, PO Box 51178, Riyadh 11543
Country: SAUDI ARABIA
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E-mail address: ykhlef@ksu.edu.sa
Other E-mails: ykhlef@yahoo.fr
Title of the Paper: Mining Sequential Patterns Using Genetic Algorithm and Particle Swarm Optimization
Authors as they appear in the Paper: Mourad Ykhlef, Hebah Elgibreen
Email addresses of all the authors:
Number of paper pages: 11
Abstract: Sequential pattern mining is an important task in data mining field, which describes potential sequenced relationships among items in a database. There are many different algorithms introduced for this task. It can be classified into two categories. The first category is concerned with conventional methods and the other employs evolutionary based approaches. Sequential patterns conventional algorithms can find the exact optimal sequential pattern rule but it takes a long time, especially when they are applied on large database. On the other hand, Evolutionary Algorithms can find good sequential pattern rules in a very short time comparing to the conventional algorithms. Time is the most important factor in this task especially when the results are needed in a limited period of time. This article will introduce a new kind of hybrid evolutionary algorithm that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to mine Sequential Pattern, in order !
to improve the speed of evolutionary algorithms convergence. This algorithm is referred to as SP-GAPSO.
Keywords: Data mining, Genetic algorithm, Hybrid evolutionary algorithm, Particle swarm optimization algorithm, Pharmacy database, Sequential pattern mining.
EXTENSION of the file: .pdf
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