An Implementation of Hybrid Genetic Algorithm for Clustering based Data for Web Recommendation System

Authors

  • Animesh Shrivastava Department of CSE, Shri Vaishnav Institute of Technology & Science, Indore, India
  • Singh Rajawat Department of CSE, Shri Vaishnav Institute of Technology & Science, Indore, India

Keywords:

Recommendation Systems, k-NN; Genetic Algorithm, Clustering

Abstract

Web Mining is an interesting domain in information processing that includes a large variety of applications i.e. recommendation system design, next user web page prediction, navigational pattern analysis and others. In this paper a new hybrid clustering algorithm is proposed and implemented using Genetic algorithm and K-NN algorithm and the implementation of desired algorithm is given using a web recommendation system which analyze user navigational pattern from web server access log file and recommends the next user web page. The performance of the designed system is evaluated and listed in this paper. According to the results, the proposed hybrid approach is efficient and effective for the given application domain.

References

Jaideep Shrivastava, Robert Cooley, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data” SIGKDD Explorations,ACM SIGKDD Jan 2000 Volume1 Issue 2.

L.K. Joshila Grace,V.Maheswari, Dhinaharan Nagamalai,”Analysis of web logs and web user in web mining”, International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.1, January 2011

Osama Abu Abbas,“Comparision between Data Clustering Algorithms”,The International Arab Journal of Information Technology, Vol 5, No.3, July 2008.

C.P.Sumathi et. al,“Automatic Recommendation of Web Pages in Web usage mining” International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 3046-3052

Tapas Kanungo, Nathan S. Netanyahu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation” IEEE transactions on pattern analysis and machine intelligence, Vol. 24, no. 7, July 2002

Hassan H. Malik, and John R. Kender, “Classification by Pattern-Based Hierarchical Clustering”, Department of Computer Science, Columbia University, New York, NY 10027, USA{hhm2104, jrk}@cs.columbia.edu

László Kozma Lkozma@cis.hut.fi,“k Nearest Neighbors algorithm” Helsinki University of Technology T-61.6020 Special Course in Computer and Information Science 20. 2. 2008

Olga Georgiou,Nicolas Tsapatsoulis, “Improving the Scalability of Recommender Systems by Clustering Using Genetic Algorithms”, Volume 6352, 2010, pp 442-449 @Springer-Verlag Berlin Heidelberg ICANN 2010

Ujjwal Maulik,Sanghamitra Bandyopadhyay, “Genetic algorithm based clustering technique”,PII: S 0 0 3 1 - 3 2 0 3 ( 9 9 ) 0 0 1 3 7 – 5@2000 Pattern Recognition Society. Published by Elsevier Science Ltd.

Petra Kudov´a,“Clustering Genetic Algorithm”,18th International Workshop on Database and Expert Systems Applications DOI 10.1109/DEXA.2007.65

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Published

2014-04-30

How to Cite

[1]
A. Shrivastava and S. Rajawat, “An Implementation of Hybrid Genetic Algorithm for Clustering based Data for Web Recommendation System”, Int. J. Comp. Sci. Eng., vol. 2, no. 4, pp. 6–11, Apr. 2014.

Issue

Vol. 2 No. 4 (2014): IJCSE April Edition

Section

Research Article

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