TY - JFULL
AU - Mourad Ykhlef and Hebah ElGibreen
PY - 2009/1/
TI - Mining Sequential Patterns Using Hybrid Evolutionary Algorithm
T2 - International Journal of Computer and Information Engineering
SP - 2938
EP - 2946
VL - 3
SN - 1307-6892
UR - https://publications.waset.org/pdf/3393
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 36, 2009
N2 - Mining Sequential Patterns in large databases has become
an important data mining task with broad applications. It 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. Conventional algorithms
can find the exact optimal Sequential Pattern rule but it takes a
long time, particularly when they are applied on large databases.
Nowadays, some evolutionary algorithms, such as Particle Swarm
Optimization and Genetic Algorithm, were proposed and have been
applied to solve this problem. This paper 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.
ER -