Research Article
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Year 2017, Volume: 13 Issue: 4, 873 - 881, 29.12.2017

Abstract

References

  • [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)

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

Year 2017, Volume: 13 Issue: 4, 873 - 881, 29.12.2017

Abstract

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.


References

  • [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)
There are 10 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Turgay Bilgin

Mustafa Aytekin This is me

Publication Date December 29, 2017
Published in Issue Year 2017 Volume: 13 Issue: 4

Cite

APA Bilgin, T., & Aytekin, M. (2017). Web Proxy Log Data Mining System for Clustering Users and Search Keywords. Celal Bayar University Journal of Science, 13(4), 873-881. https://doi.org/10.18466/cbayarfbe.330088
AMA Bilgin T, Aytekin M. Web Proxy Log Data Mining System for Clustering Users and Search Keywords. CBUJOS. December 2017;13(4):873-881. doi:10.18466/cbayarfbe.330088
Chicago Bilgin, Turgay, and Mustafa Aytekin. “Web Proxy Log Data Mining System for Clustering Users and Search Keywords”. Celal Bayar University Journal of Science 13, no. 4 (December 2017): 873-81. https://doi.org/10.18466/cbayarfbe.330088.
EndNote Bilgin T, Aytekin M (December 1, 2017) Web Proxy Log Data Mining System for Clustering Users and Search Keywords. Celal Bayar University Journal of Science 13 4 873–881.
IEEE T. Bilgin and M. Aytekin, “Web Proxy Log Data Mining System for Clustering Users and Search Keywords”, CBUJOS, vol. 13, no. 4, pp. 873–881, 2017, doi: 10.18466/cbayarfbe.330088.
ISNAD Bilgin, Turgay - Aytekin, Mustafa. “Web Proxy Log Data Mining System for Clustering Users and Search Keywords”. Celal Bayar University Journal of Science 13/4 (December 2017), 873-881. https://doi.org/10.18466/cbayarfbe.330088.
JAMA Bilgin T, Aytekin M. Web Proxy Log Data Mining System for Clustering Users and Search Keywords. CBUJOS. 2017;13:873–881.
MLA Bilgin, Turgay and Mustafa Aytekin. “Web Proxy Log Data Mining System for Clustering Users and Search Keywords”. Celal Bayar University Journal of Science, vol. 13, no. 4, 2017, pp. 873-81, doi:10.18466/cbayarfbe.330088.
Vancouver Bilgin T, Aytekin M. Web Proxy Log Data Mining System for Clustering Users and Search Keywords. CBUJOS. 2017;13(4):873-81.