Bees Swarm Optimization for Web Association Rule Mining

被引:32
作者
Djenouri, Y. [1 ]
Drias, H. [1 ]
Habbas, Z. [1 ,2 ]
Mosteghanemi, H. [1 ,2 ]
机构
[1] USTHB Univ Algiers, LRIA, BP 32 El Alia Bab Ezzouar, Algiers, Algeria
[2] Univ Lorraine, LITA, F-57045 Metz, France
来源
2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY WORKSHOPS (WI-IAT WORKSHOPS 2012), VOL 3 | 2012年
关键词
Association rule mining; Genetic metaheuristic; BSO metaheuristic; Solution Quality; Optimization Problem; Web Mining;
D O I
10.1109/WI-IAT.2012.148
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper deals with Association Rules Mining algorithms for very large databases and especially for those existing on the web. The numerous polynomial exact algorithms already proposed in literature treated somehow in an efficient way data sets with reasonable size. However they are not capable to cope with a huge amount of data in the web context where the respond time must be very short. This paper, mainly proposes two new Association Rules Mining algorithms based on Genetic metaheuristic and Bees Swarm Optimization respectively. Experimental results show that concerning both the fitness criterion and the CPU time, IARMGA algorithm improved AGA and ARMGA two other versions based on genetic algorithm already proposed in the literature. Moreover, the same experience shows that concerning the fitness criterion, BSO-ARM achieved slightly better than all the genetic approaches. On the other hand, BSO-ARM is more time consuming. In all cases, we observed that the developed approaches yield useful association rules in a short time when comparing them with previous works.
引用
收藏
页码:142 / 146
页数:5
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