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Web Usage Mining: A Concise Survey on Tools and Applications

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 74 - Number 1
Year of Publication: 2013
Arun Kumar Singh
Dheeraj Sharma
Avinav Pathak

Arun Kumar Singh, Dheeraj Sharma and Avinav Pathak. Article: Web Usage Mining: A Concise Survey on Tools and Applications. International Journal of Computer Applications 74(1):1-7, July 2013. Full text available. BibTeX

	author = {Arun Kumar Singh and Dheeraj Sharma and Avinav Pathak},
	title = {Article: Web Usage Mining: A Concise Survey on Tools and Applications},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {74},
	number = {1},
	pages = {1-7},
	month = {July},
	note = {Full text available}


Web usage mining focuses on techniques that might predict user behavior whereas the user interacts with the net. It tries to create sense of the info generated by the net surfer's sessions or behaviors. There has been an effort to supply a summary of the state of the art within the analysis of internet usage mining, whereas discussing the foremost relevant tools obtainable within the sphere likewise because the niche needs that this form of tools lack. It offers an outlook on the prevailing tools, their specialized focus with reference to the practical objectives and also the would like for a additional comprehensive new entrant during this sphere within the light-weight of this state of affairs. In the end, the paper are finished by listing some challenges and future trends during this analysis space. Overall the main target of the paper are to gift a survey of the recent developments during this space that is obtaining an excessive amount of attention from internet development arena.


  • O. Etzioni, The World-Wide Web: quagmire or gold mine?, Communications of the ACM, 39(11), 1996,65-68 .
  • S. K. Madria, S. S. Bhowmick, W. K. Ng, and E. Lim, Research Issues in Web Data Mining, Data Warehousing and Knowledge Discovery, 1999, 303- 312.
  • J. Borges, and M. Levene, Data Mining of User Navigation Patterns', Web Usage Analysis and User Profiling, San Diego, CA, USA, 2000, 31-39.
  • S. Chakrabarti, B. E. Dom, S. Ravi Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins, D. Gibson, and J. Kleinberg,Mining the Web's Link Structure', Computer, 32(8), 1999, 60-67. Int. J. Advanced Networking and Applications 1428Volume:03 Issue:06 Pages:1422-1429 (2012) ISSN : 0975-0290.
  • T. Joachims, D. Freitag, T. Mitchell, WebWatcher: A Tour Guide for the World Wide Web, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Morgan Kaufmann, 15, 1997, 770-777.
  • J. Srivastava, R. Cooley, M. Deshpande, P. Tan, Web usage mining: Discovery and applications of usage patterns from web data' SIGKDD Explorations newsletter, 1(2), 2000, 12-23.
  • B. Mobasher, R. Cooley, and J. Srivastava, J. Automatic personalization based on Web usage mining, Communications of the ACM, 43(8), 2000, 142-151.
  • M. Eirinaki and M. Vazirgiannis , Web mining for web Personalization, ACM Transactions on Internet Technology, 3(1), 2000, 1-27.
  • B. Masand, M. Spiliopoulou, J. Srivastava, and O. R. Zaiane, Web Mining for Usage Patterns & Profiles,WEBKDD02 , SIGKDD Explorations, 4(2), 2002, 125-127 .
  • A. G. Buchner, M. Baumgarten, , S. S Anand, M. D. Mulvenna, and J. G. Hughes, Navigation PatternDiscovery from Internet Data, Proceedings of Web Usage Analysis and User Profiling at the International WEBKDD99 Workshop, 2000, 74- 91.
  • S. Baron, and M. Spiliopoulou, Monitoring the Evolution of Web Usage Patterns, EWMF 2003, 181- 183.
  • M. Spiliopoulou, Data mining for the Web, Proceedings of the Third European conference, PKDD'99, 1999, 588-589.
  • L. Chen, and K. Sycara, WebMate: A Personal Agent for Browsing and Searching, Proceedings of the 2ndInternational Conference on Autonomous Agents, Minneapolis MN, USA, 1999, 132-139.
  • J. I. Hong, , J. Heer, S. Waterson, and J. A. Landay, WebQuilt: A proxy-based approach to remote webusability testing, ACM Transactions on Information Systems, 19(3), 2001, 263-285.
  • M. Koutri, N. Avouris, and S. Daskalaki, A survey on web usage mining techniques for web-basedadaptive hypermedia systems, Adaptable and Adaptive Hypermedia Systems Idea, 2004, 1-23.
  • D. Pierrakos, G. Paliouras, C. Papatheodorou, and C. D. Spyropoulos, Web usage mining as a tool for personalization: A survey, User Modeling and UserAdapted Interaction, 13(4), 2003, 311-372. Kluwer Academic Publishers.
  • R. Kosala, and H. Blockeel, Web Mining Research: A Survey', Machine Learning, 2(1), 2000, 1-15.
  • R. Cooley, B. Mobasher, and J. Srivastava, Web mining: information and pattern discovery on the World Wide Web', Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence, 97(2. 1), 1997, 558-567.
  • O. R. Zaiane, Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries ADL98, IEEE Computer Society, Santa Barbara, CA,1998, 19-29.
  • R. Cooley, Web Usage Mining: Discovery and Application of Interesting Patterns from Web data, PhD thesis, Dept. of Computer Science, University of Minnesota, USA, 2000.
  • R. S. T. Lee, and J. N. K. Liu, iJADE Web-Miner: An Intelligent Agent Framework for Internet Shopping, IEEE Transactions on Knowledge and Data Engineering, 16(4), 2004, 461- 473.
  • B. Mobasher, H. Dai, T. , Luo, and M. Nakagawa, Effective personalization based on association rule discovery from web usage data', Proceeding of the third international workshop on Web information and data management WIDM 01, 9, USA, 2001, 9-15
  • F. Toolan, and N. Kusmerick, Mining Web Logs for Personalized Site Maps, Proceedings of the ThirdInternational Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)(WISEW '02). IEEE Computer Society, Washington, DC, USA, 2002, 232-237.
  • L. Lu, M. H. Dunham, and Y. Meng, Discovery of Significant Usage Patterns from Clusters ofClickstream Data, Proceedings of the ACM SIGKDD Workshop on Knowledge Discovery in WebWebKDD05, 2005, Chicago, IL, USA.
  • T. Falkowski, J. Bartelheimer, and M. Spiliopoulou, Mining and Visualizing the Evolution of Subgroups in Social Networks, Proceedings of the 2006 IEEEWICACM International Conference on WebIntelligence, 2006, 52-58.
  • N. Labroche, M. J. Lesot, and L. Yaffi, A New Web Usage Mining and Visualization Tool', 19th IEEE International Conference on Tools with Artificial Intelligence ICTAI 2007, 1, 2007, 321-328.
  • F. Khalil, J. Li, and H. Wang, Integrating Recommendation Models for Improved Web Page Prediction Accuracy', Reproduction, 74(Acsc), Australian Computer Society, Inc. ACM International Conference Proceeding Series, Wollongong, Australia, Vol. 312, 2008, 91-100.
  • Y. Tao, T. Hong, and Y. Su, Web usage mining with intentional browsing data, Expert Systems with Applications, 34(3), 2008, 1893-1904.
  • O. Nasraoui, M. Soliman, E. Saka, A. Badia, and R. Germain, A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites', IEEE Transactions on Knowledge and Data Engineering, 20(2), 2008, 202-215.
  • F. Masseglia, P. Poncelet, M. Teisseire, and A. Marascu, Web usage mining: extracting unexpected periods from web logs, Data Mining and Knowledge Discovery, 16(1), 2008, 39-65.