{"title":"Mining Sequential Patterns Using Hybrid Evolutionary Algorithm","authors":"Mourad Ykhlef, Hebah ElGibreen","country":null,"institution":"","volume":36,"journal":"International Journal of Computer and Information Engineering","pagesStart":2939,"pagesEnd":2947,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/3393","abstract":"Mining Sequential Patterns in large databases has become\r\nan important data mining task with broad applications. It is\r\nan important task in data mining field, which describes potential\r\nsequenced relationships among items in a database. There are many\r\ndifferent algorithms introduced for this task. Conventional algorithms\r\ncan find the exact optimal Sequential Pattern rule but it takes a\r\nlong time, particularly when they are applied on large databases.\r\nNowadays, some evolutionary algorithms, such as Particle Swarm\r\nOptimization and Genetic Algorithm, were proposed and have been\r\napplied to solve this problem. This paper will introduce a new kind\r\nof hybrid evolutionary algorithm that combines Genetic Algorithm\r\n(GA) with Particle Swarm Optimization (PSO) to mine Sequential\r\nPattern, in order to improve the speed of evolutionary algorithms\r\nconvergence. This algorithm is referred to as SP-GAPSO.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 36, 2009"}