An Efficient Genetic Simulated Annealing Association Rules Method

Jun Jie Cen; Guo Hong Gao; Ying Jun Wang

doi:10.4028/www.scientific.net/AMM.34-35.927

Paper Titles

Ball-End Cutter Machining Process Analysis in Five-Axis Milling
p.903

A Method of WAV Broadcast Using the I2S of LPC2300 Series ARM Chipset
p.909

A Multi-Layer Trust Management in Open Networks
p.915

Scene Detection in Videos Using Mutual Information
p.920

An Efficient Genetic Simulated Annealing Association Rules Method
p.927

An Efficient Multi-Hop Clustering Routing Algorithm in Wireless Sensor Networks
p.932

P2P Technology Application and Research of Grid Computing
p.937

Preparation and Mechanics Properties of MR Elastomers Based on Silicone Rubber
p.942

Transient Performance Simulation of Single-Cylinder Engines Based on Tribology Behaviors
p.946

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 34-35An Efficient Genetic Simulated Annealing...

An Efficient Genetic Simulated Annealing Association Rules Method

Abstract:

Association rule is one of the important models of Web mining. By analyzing the topology of web site, this paper brings forward an efficient genetic simulated annealing association rules method．It applies genetic algorithm，incremental mining technology to trace users access behavior and optimizes association rules，and forecast capable association rules which improves its precision．Finally, this paper gives out the data analysis of experiment and summarizes the characteristics of genetic mining．

You might also be interested in these eBooks

Mechanical Engineering and Green Manufacturing

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 34-35)

Pages:

927-931

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.34-35.927

Citation:

Cite this paper

Online since:

October 2010

Authors:

Jun Jie Cen, Guo Hong Gao, Ying Jun Wang

Keywords:

Association Rule, Genetic Algorithm (GA), Simulated Annealing (SA)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J. Srivastava,R. Cooley,M. Deshpande. Web usage mining: discovery and application of usage patterns from Web data. SIGKDD Explorations, 2000, 22(1): 34-40.

DOI: 10.1145/846183.846188

Google Scholar

[2] Mobasher B, Dai h\H, Luo T, Nakagawa ME. active Personalization Based on Association Rule Discovery from Web Usage Data. In proc. of the 3rd ACM workshop on Web Information and Data Managerment(WIDM 01), Atlanta, Georgia, Nov. (2001).

DOI: 10.1145/502932.502935

Google Scholar

[3] Liuyong Guo, Xue-ming, Zhang Wei. Based on genetic algorithm feature subset selection [J]. Computer Engineering, 2003, 29 (6) : 67-70.

Google Scholar

[4] YU Hong-mei , YAO Ping-jing. Combined genetic algorithm/ simulated annealing algorithm for large-scale system energy integration [J] . Computers and Chemical Engineering , Elsevier Science Ltd , 2000 , 8(24) : 2023 - (2035).

DOI: 10.1016/s0098-1354(00)00601-3

Google Scholar

[5] C.F. Juan hybrid of genetic algorithm and particle swarm optimization for recurrent network design[J], IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics, 2004, 34(2): 997-1006.

DOI: 10.1109/tsmcb.2003.818557

Google Scholar

[6] Wang Xuemei, Wang and. Simulated annealing and genetic algorithms combined with [J]. Journal of Computers, 1997, 20 (4) : 381-384.

Google Scholar

[7] Shu Wan-neng, Zheng Shi- jue. A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling [J]. Wuhan University Journal of Natural Sciences，2006, 12(5): 56-60.

DOI: 10.1007/bf02829270

Google Scholar

[8] C.W. Ahn, R.S. Ramakrishna, A genetic algorithm for shortest path routing problem and the sizing of populations, IEEE Transactions on Evolutionary Computation 2002, 6 (6): 107-116.

DOI: 10.1109/tevc.2002.804323

Google Scholar