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IJML 2012 Vol.2(3): 219-221
doi: 10.7763/IJMLC.2012.V2.117

Clustering Web Logs Using Similarity Upper Approximation with Different Similarity Measures

Rajhans Mishra , Pradeep Kumar

  • Information Technology & Systems Department, Indian Institute of Management Lucknow, IIM Lucknow Uttar Pradesh- India.

Abstract

In this paper we adopted the similarity upper approximation based clustering of web logs using various similarity/distance metrics. The paper shows the viability of our methodology. Web logs capture the information about web sites as well the sequence of the visit. Sequence of visit provides an important insight about the behavior of the user. Rough set, a soft computing technique, deals with vagueness present in data. It captures the indiscernibility at different levels of granularity. The paper has shown the results on msnbc data set with different similarity measures along with explanation of results.

Keywords

  • Clustering
  • sequential data
  • similarity upper approximation
117-CL091

How to Cite

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Rajhans Mishra and Pradeep Kumar, "Clustering Web Logs Using Similarity Upper Approximation with Different Similarity Measures," International Journal of Machine Learning, vol. 2, no. 3, pp. 219-221, 2012. https://doi.org/10.7763/IJMLC.2012.V2.117

Copyright & License

Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD