| Paper title: |
Offline Web Mining Analysis on Various Site Types Using Classical Algorithms |
| Published in: | Issue 1, (Vol. 4) / 2010 |
| Publishing date: | 2010-03-30 |
| Pages: | 9-13 |
| Author(s): | Dan A. Sitar-Taut, Daniel Mican |
| Abstract. | Abstract-Web Mining aims to discover new information from the web links structures, the content pages and the way in which the users interact with the database. Current concern in the domain of online applications deals with the possibility of extracting knowledge, evaluating the user's behavior and understanding what they desire, in order to anticipate immediately their navigational behavior. In our experiment we will apply techniques such as clustering and the association rules, in order to discover new patterns among the existent data, with the purpose of making correlations and organizing the data by creating new groups of similar objects, in order to access and retrieve the data in accordance with the user's preferences. |
| Keywords: | Knowledge Management, Web Mining, Session Construction, Association Rules, Clustering |
| References: | 1. R. Agrawal, T. Imieliñski, and A. Swami, “Mining association rules between sets of items in large databases”, In Proceedings of the 1993 ACM SIGMOD international Conference on Management of Data, Washington, D.C., United States, 1993, pp. 207-216. 2. R. Agrawal, and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases”, In Proceedings of the 20th international Conference on Very Large Data Bases, 1994, pp. 487-499. 3. M. Baglioni, U. Ferrara, A. Romei, S. Ruggieri, and F. Turini, “Preprocessing and mining web log data for web personalization,” ser. Lecture Notes in Computer Science, vol. 2829, 2003, Online.. Available: http://www.springerlink.com/content/7at3ta2agrflf0a9/ 4. L. D. Catledge and J. E. Pitkow, “Characterizing browsing behaviors on the world-wide web,” Tech. Rep, 1995, Online.. Available: http://hdl.handle.net/1853/3558 5. A. Ceglar, and J. F. Roddick, “Association mining”. ACM Comput. Surv. 38, 2, 2006. 6. M. S. Chen, J. S. Park, and P. S. Yu, “Efficient data mining for path traversal patterns”, IEEE Transactions on Knowledge and Data Engineering, 10(2), 1998, pp. 209- 221. 7. R. Cooley, M. Deshpande, J. Srivastava, and P.N. Tan, “Web usage mining: Discovery and applications of usage patterns from web data”, ACM SIGKDD Explorations, 2000. 8. J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions”, Data Min. Knowl. Discov. 15, 1, 2007, pp. 55-86. 9. A. K. Jain, and R. C. Dubes, “Algorithms for clustering data”, NJ: Prentice Hall, 1988 10. R. Kosala and H. Blockeel, “Web mining research: A survey”, ACM SIGKDD, 2(1):1-15, 2000. 11. Y. Li, B. Feng, “The Construction of Transactions for Web Usage Mining,” cinc, vol. 1 2009 International Conference on Computational Intelligence and Natural Computing, 2009, pp. 121-124. 12. B. Liu, “Web Data Mining Exploring Hyperlinks, Contents, and Usage Data”, Springer-Verlag Berlin Heidelberg, 2007. 13. J. B. MacQueen, “Some methods for classification and analysis of multivariate observations”, In Proceedings fifth Berkeley symposium on mathematical statistics and probability, California, USA, 1967, pp. 281–297. 14. D. Mican, D. A. Sitar-Taut, “Preprocessing and Content/Navigational Pages Identification as Premises for an Extended Web Usage Mining Model Development”, Informatica Economicã Journal, Vol. 13, No. 4, 2009, pp. 168-179. 15. A. Vakali, G. Pallis, “Web Data Management Practices: Emerging Techniques and Technologies”, Idea Group Publishing, 2007. 16. I. H. Witten and E. Frank, “Data Mining: Practical machine learning tools and techniques”, 2nd Edition, Morgan Kaufmann, San Francisco, 2005. |
| Back to the journal content | |
