Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: Editor-in-chief: Minhua Ma, Patricia Goncalves
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Journal of Computer Sciences and Applications. 2013, 1(3), 39-45
DOI: 10.12691/jcsa-1-3-3
Open AccessReview Article

Extracting Users’Navigational Behavior from Web Log Data: a Survey

Maryam Jafari1, Farzad SoleymaniSabzchi1, and Shahram Jamali2

1Sama Technical and Vocational College, Islamic Azad University, Ardabil Branch, Ardabil, Iran

2Computer Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran

Pub. Date: May 10, 2013

Cite this paper:
Maryam Jafari, Farzad SoleymaniSabzchi and Shahram Jamali. Extracting Users’Navigational Behavior from Web Log Data: a Survey. Journal of Computer Sciences and Applications. 2013; 1(3):39-45. doi: 10.12691/jcsa-1-3-3


Web Usage Mining (WUM) is a kind of data mining method that can be used to discover user access patterns from Web log data. A lot of research has been done already about this area and the obtained results are used in different applications such as recommending the Web usage patterns, personalization, system improvement and business intelligence. WUM includes three phases that are called preprocessing, pattern discovery and pattern analysis. There are different techniques for WUM that have their own advantages and disadvantages. This paper presents a survey on some of the existing WUM techniques and it is shown that how WUM can be applied to Web server logs.

web usage mining web log mining pattern discovery preprocessing sequence mining

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[1]  Cooley, R., Mobasher, B. and Srivastava, J. “Web mining: information and pattern discovery on the World Wide Web,” in International Conference on Tools with Artificial Intelligence, Newport Beach, IEEE, 1997.
[2]  Cooley, R., Mobasher, B. and Srivastava, J. “Data preparation for mining World Wide Web browsing patterns,” in Journal of Knowledge and Information System, 1999.
[3]  Srivastava, J., et al. “Web usage mining: discovery and applications of usage patterns from web data,” in SIGKDD Explorations 1, 2000.
[4]  Etzioni, O. “The World-wide web: Quagmire or gold mine,” in Communication of the ACM, 1996, 65-68.
[5]  Chen, M., Park, J. and Yu, P. “Data mining for path traversal patterns in a Web environment,” in International Conference on Distributed Computing Systems, 1996, 385-392.
[6]  Chen, M., Park, J. and Yu, P. “Efficient data mining for path traversal patterns,” in IEEE Transactions on knowledge and data engineering, 1998, 209-221.
[7]  Mannila, H. and Toivonen, H. “Discovering generalized episodes using minimal occurrences,” in International Conference on Knowledge and Data Mining, 1996, 146-151.
[8]  Yan, T., et al. “From user access patterns to dynamic hypertext linking,” in International World Wide Web conference on Computer networks and ISDN systems, 1996, 1007-1014.
[9]  Zhu, J., Hong, J and Hughes, J. “Using Markov Chains for Link Prediction in Adaptive Web Sites,” in Lecture Notes in computer science, 2002, 60-73.
[10]  Jalali, M., et al. “A new clustering approach based on graph partitioning for navigation patterns mining,” in International Conference on Pattern Recognition, 2008, 1-4.
[11]  Sujatha, V. and Punithavalli. “Improved User Navigation Pattern Prediction Technique From Web Log Data,” in International Conference on Communication Technology and System Design, 2001, 92-99.
[12]  Tug, E., Sakiroglu, M. and Arslan, A. “Automatic discovery of the sequential accesses from web log data files via a genetic algorithm,” in Knowledge-Based Systems, 2006, 180-186.
[13]  Kim, S. and Zhang, B. “Genetic mining of HTML structures for effective web-document retrieval,” in Applied Intelligence 18, 2003, 243-256.
[14]  Picarougne, N., et al. “Web Mining with Genetic-Based Algorithm,” in NEC Research Institute CiteSeer, 2002.
[15]  Abraham, A. and Ramos, V. “Web usage mining using artificial ant colony clustering and genetic programming,” in IEEE Congress on Evolutionary Computation - CEC, 2003, 1384-1391.
[16]  Zhou, B., Hui, S.C. and Chang, K. “An Intelligent recommender system using sequential web access patterns,” in Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, 2004, 1-3.
[17]  Burke, R. “Hybrid recommender systems: survey and experiments,” in User Modeling and User-Adapted Interaction, 2002, 331-370.
[18]  Ishikawa, H., et al. “An intelligent web recommendation system: A web usage mining approach,” in ISMIS, 2002, 342-350.
[19]  Mobasher, B., et al., “Integrating web usage and content mining for more effective personalization,” in First International Conference on Electronic Commerce and Web Technologies, 2000, 165-176.
[20]  Sarukkai, R.R. “Link prediction and path analysis using Markov chains,” in 9th World Wide Web conference, 1999.
[21]  Chen, M.S., Park, J.S. and Yu, P.S. “Data mining for path traversal patterns in a web enviroment,” in 16th International Conference on Distributed Computing Systems, 1996, 385-392.
[22]  Bonchi, F., et al. “Web log data warehousing and mining for intelligent web caching,” in Data and knowledge engineering, 2001.
[23]  Schechter, S., Krishnan, M. and Smith, M.D. “Using path profiles to predict HTTP requests,” in Seventh International Conference on World Wide Web, 1998.
[24]  Dean, J. and Henzinger, M.R. “Finding related pages in the world wide web,” in Eighth International Conference on World Wide Web, 1999.
[25]  Chen, M., LaPaugh, A.S. and Singh, J.P. “Predicting category accesses for a user in a structured information space,” in 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2002.
[26]  Brin, S. and Page, L. “The anatomy of a large-scale hypertextual web search engine,” in Seventh Int. Conf. on World Wide Web, 1998.
[27]  Qiu, F. and Cho, J. “Automatic identification of user interest for personalized search,” in 15th Int. Conf. on World Wide Web, WWW’06, 2006.
[28]  Eirinaki, M. and Vazirgianis, M. “Web mining for web personalization,” in ACM Trans. Internet Tehnol. (TOIT), 2003.
[29]  Suneetha, K.R. and Krishnamoorthi, R. “Identifying User Behavior by Analyzing Web Server access log file,” in IJCSNS International Journal of Computer Science and Network Security, 2009.
[30]  Facca, F.M. and Lanzi, P.L. “Mining interesting knowledge from weblogs: a survey,” in Data Knowledge Eng. 53, 2005.
[31]  Dong, D. “Exploration on Web Usage Mining and its Application,” in IEEE, 2009.
[32]  Wang, Y. “Web Mining and Knowledge Discovery of Usage Patterns,” in CS 748T Project, 2000.
[33]  Tanasa, D. and Trousse, B. “Advanced data preprocessing for inter sites Web usage mining,” in Intelligent Systems, IEEE, 2004, 59-65.
[34]  Cooley, R. and Mobasher, B. “Data Preparation for Mining World Wide Web Browsing Patterns,” in Knowledge and Information Systems, 1999.
[35]  Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. “From data mining to knowledge discovery: An overview,” in Proc. ACM KDD, 1994.
[36]  Agrawal, R. and Srikant, R. “Fast algorithms for mining association rules,” in Proc. of the 20th VLDB Conference, 1994.
[37]  Joshi, K.P., Joshi, A. and Yesha, Y. “On using a warehouse to analyze web logs,” in Distributed and Parallel Databases, 2003, 161-180.
[38]  Han, J. andKamber, M. “Data Mining Concepts and Techniques,”in the Morgan Kaufmann Series in Data Management Systems, 2001.
[39]  Huang, X. and Cercone, N. “Comparison of interestingness functions for learning web usage patterns,” in Eleventh International Conference on Information and Knowledge Management, 2000, 617-620.
[40]  Wong, S.S.C. and Pal, S. “Mining fuzzy association rules for web access case adaptation. Wong, S.S.C. and Pal, S,” in Workshop on Soft Computing in Case-Based Reasoning, International Conference on Case Based Reasoning, 2001.