Web Proxy Log Data Mining System for Clustering Users and Search Keywords

In this study, Internet users were clustered by the search keywords which they type into search bars of search engines. Our proposed software is called UQCS (User Queries Clustering System) and it was developed to demonstrate the efficiency of our hypothesis. UQCS co-operates with the Strehl’s relationship based clustering toolkit and performs segmentation on users based on the keywords they use for searching the web. Internet Proxy server logs were parsed and query strings were extracted from the search engine URL’s and the resulting IP-Term matrix was converted into a similarity matrix using Euclidean, Jaccard, Cosine Distance and Pearson Correlation Distance metrics. K- Means and graph-based OPOSSUM algorithm were used to perform clustering on the similarity matrices. Results were illustrated by using CLUSION visualization toolkit.

Keywords:

Data mining, Document clustering Graph clustering, web mining,

PDF

___

[1] Sankar K.Pal, Varun Talwar, Pabitra Mitra, “Web Mining in Soft Computing Framework:Relevance, State of the Art and Future Directions”, IEEE Transactions on Neural Networks, Vol.13, No.5, September 2002
[2] O.Etzioni. “The World Wide Web: Quagmire or Gold Mining”, Communicate of the ACM, (39)11:65-68, 1996;
[3] Kosala and Blockeel, “Web mining research: A sur-vey,” SIGKDD:SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery and Data Mining, ACM, Vol. 2, 2000
[4] Qingyu Zhang and Richard s. Segall,” Web mining: a survey of current research,Techniques, and software”, in the International Journal of Information Technology & Decision Making Vol. 7, No. 4 (2008) 683– 720
[5] Chun-Ling Zhang, Zun-Feng Liu, Jing-Rui Yin, “The Application Research on Web Log Mining in E-Marketing”, Hebei Polytechnic University, 978-1-4244-5895-0 IEEE 2010
[6] Strehl, Alexander, “Relationship-based Clustering and Cluster Ensembles for High-dimensional Data Mining”, 2002 Doctoral Dissertation, University of Texas
[7] A. Strehl and J. Ghosh, "Relationship-based Cluster-ing and Visualization for High-dimensional Data Min-ing", INFORMS Journal on Computing, pages 208-230, Spring 2003
[8] G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal of Scientific Computing, 20(1):359–392, 1998.
[9] http://code.google.com/p/zemberek/ (Access Date: 08.10.2016)
[10]http://strehl.com/soft.html (Access Date: 10.10 .2016)