A web page usage prediction scheme using sequence indexing and clustering techniques

Data and Knowledge Engineering

Volumn 69, Issue 4, 2010, Pages 371-382

  Dimopoulos, Costantinos ^a   Makris, Christos ^a   Panagis, Yannis ^a   Theodoridis, Evangelos ^a   Tsakalidis, Athanasios ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

On line web page recommendation; Web mining; Weighted sequences; World Wide Web

Indexed keywords

CACHE SYSTEMS; CLUSTERING TECHNIQUES; COMPUTATIONAL EFFORT; LOG FILE; MEMORY SPACE; PREDICTION SCHEMES; QUALITY PERFORMANCE; RECOMMENDATION SYSTEMS; SUFFIX-TREES; WEB MINING; WEB PAGE; WORKING ENVIRONMENT;

NAVIGATION;

WEBSITES;

EID: 76349084477     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2009.04.010     Document Type: Article

References (52)

1. A. Banerjee, J. Ghosh, Clickstream clustering using weighted longest common subsequences, in: Proc. of the Web Mining Workshop at the 1st SIAM Conference on Data Mining, 2001.
2. S. Brin, L. Page, The anatomy of a large-scale hypertextual web search engine, in: Proceedings of the Seventh Int. Conf. on World Wide Web, vol. 7, 1998, pp. 107-117.
3. Catledge L., and Pitkow J. Characterizing browsing behaviors on the WWW. Comput. Networks ISDN Syst. (1995) 1065-1073
4. S. Chakrabarti, M. van der Berg, B. Dom, Focused crawling: a new approach to topic-specific web resource discovery, in: Proceedings of 8th Int. World Wide Web Conf. (WWW8), 1999.
5. M. Chen, A.S. LaPaugh, J.P. Singh, Predicting category accesses for a user in a structured information space, in: Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2002.
6. Christopoulou E., Garofalakis J., Makris C., Panagis Y., Psaras-Chatzigeorgiou A., Sakkopoulos E., and Tsakalidis A. Techniques and metrics for improving website structure. J. Web Eng. 2 1-2 (2003) 90-114
7. Cooley R., Mobasher B., and Srivastava J. Data preparation for mining WWW browsing patterns. Knowledge Inform. Syst. (1999)
8. Dean J., and Henzinger M.R. Finding related pages in the world wide web. In: Enslow P.H. (Ed). Proceeding of the Eighth International Conference on World Wide Web (Toronto, Canada) (1999), Elsevier/North-Holland, New York, NY 1467-1479
9. Deshpande M., and Karypis G. Selective Markov models for predicting web page accesses. ACM Trans. Internet Tech. 4 2 (2004) 163-184
10. Dunham M. Data Mining Introductory and Advanced Topics (2003), Prentice Hall
11. Eirinaki M., and Vazirgianis M. Web mining for web personalization. ACM Trans. Internet Tehnol. (TOIT) 3 1 (2003)
12. Eysenecj M., and Keane M. Cognitive Psychology (1990), Lawrence Erlbaum Associates
13. Facca F.M., and Lanzi P.L. Mining interesting knowledge from weblogs: a survey. Data Knowledge Eng. 53 3 (2005) 225-241
14. Fredkin E. Trie Memory. Commun. ACM 3 9 (1960) 490-499
15. A. Gulli, A. Signorini, The indexable web is more than 11.5 billion pages, in: Special interest Tracks and Posters of the 14th International Conference on World Wide Web, Chiba, Japan, 2005.
16. U. Gunduz, M.T. Ozsu, A web page prediction model based on click-stream tree representation of user behavior, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD'03, 2003.
17. J. Guo, V. Keselj, Q. Gao, Integrating web content clustering into web log association rule mining, AI 2005, LNAI 3501, 2005, pp. 182-193.
18. Gusfield D. Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology (1997), Cambridge University Press
19. A. Guttman, R-trees: a dynamic index structure for spatial searching, in: Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, 1984.
20. Hammouda K.M., and Kamel M.S. Efficient phrase-based document indexing for web document clustering. IEEE Trans. Knowledge Data Eng. 16 10 (2004) 1279-1296
21. B. Hay, G. Wets, K. Vanhoof, Clustering navigation patterns on a website using a sequence alignment method, in: Intelligent Techniques for Web Personalization: IJCAI 2001, 17th Int. Joint Conf. on Artificial Intelligence, Seattle, WA, USA, 2001, pp. 1-6.
22. Huang X., Cercone N., and An A. Comparison of interestingness functions for learning web usage patterns. Proceedings of the Eleventh International Conference on Information and Knowledge Management (2002), ACM Press 617-620
23. Iliopoulos C.S., Makris C., Panagis Y., Perdikuri K., Theodoridis E., and Tsakalidis A. The weighted suffix tree: an efficient data structure for handling molecular weighted sequences and its applications. Fundam. Inform. 71 2-3 (2006) 259-277
24. Knuth D. The Art of Computer Programming. second ed. Sorting and Searching vol. 3 (1997), Addison-Wesley
25. Kogan J. Introduction to Clustering Large and High-Dimensional Data (2007), Blackwell Publishing
26. Kumar P., Radha Krishna P., Bapi Raju.S., and De S.K. Rough clustering of sequential data. Data Knowledge Eng. 63 2 (2007) 183-199
27. Jespersen S.E., Thorhauge J., and Pedersen T.B. A hybrid approach to web usage mining. Proceedings of the 4th Int. Conf. on Data Warehousing and Knowledge Discovery (2002), Springer-Verlag 73-82
28. Joshi K.P., Joshi A., and Yesha Y. On using a warehouse to analyze web logs. Distrib. Parallel Databases 13 2 (2003) 161-180
29. Liu H., and Keselj V. Combined mining of web server logs and web contents for classifying user navigation patterns and predicting users' future requests. Data Knowledge Eng. 61 2 (2007) 304-330
30. Christos Makris, Yannis Panagis, Evangelos Theodoridis, Athanasios K. Tsakalidis, A web-page usage prediction scheme using weighted suffix trees, in: 14th International Symposium on String Processing and Information Retrieval (SPIRE2007), Santiago, Chile, 2007, pp. 242-253.
31. A. Nanopoulos, D. Katsaros, Y. Manolopoulos, Exploiting web log mining for web cache enhancement, in: R. Kohavi, B. Masand, M. Spiliopoulou, J. Srivastava (Eds.), WEBKDD 2001-Mining Web Log Data Across All Customers Touch Points, Third Int. Workshop, San Francisco, CA, USA, 2001.
32. Needleman S., and Wunsch C. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48 3 (1970) 443-453
33. Padmanabhan V.N., and Mogul J.C. Using predictive prefetching to improve world wide web latency. SIGCOMM Comput. Commun. Rev. 26 3 (1996)
34. Pei J., Han J., Mortazavi-Asl B., Pinto H., Chen Q., Dayal U., and Hsu M.-C. Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowledge Data Eng. 16 11 (2004) 1424-1440
35. J. Pei, J. Han, B. Mortazavi-asl, H. Zhu, Mining access patterns efficiently from web logs, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000, pp. 396-407.
36. F. Qiu, J. Cho, Automatic identification of user interest for personalized search, in: Proceedings of the 15th Int. Conf. on World Wide Web, WWW'06, 2006, pp. 727-736.
37. R.A. Ruddle, Using string-matching to analyze hypertext navigation, in: Proceedings of the 17th ACM Conference on Hypertext and Hypermedia (Odense, Denmark, August 22-25, 2006), HYPERTEXT'06, ACM, New York, NY, 2006, pp. 49-52.
38. Rangarajan S.K., Phoha V.V., Balagani K.S., Selmic R.R., and Iyengar S.S. Adaptive Neural Network Cluster. Web Users Comput. 37 4 (2004) 34-40
39. Shahabi C., and Chen Y.-S. Improving user profiles for e-commerce by genetic algorithms. E-Commerce Intell. Methods Stud. Fuzziness Soft Comput. 105 8 (2002)
40. Smith D., and Pricer J. Sessioninzing clickstream data. Teradataview (2000) 8-9
41. Spiliopoulou M. Web usage mining for web site evaluation. Commun. ACM 43 8 (2000) 127-134
42. Tasoulis D.K., and Vrahatis M.N. Novel approaches to unsupervised clustering through the k-windows algorithm. Knowledge Min.: Ser. Stud. Fuzziness Soft Comput. Ser.: Stud. Fuzziness Soft Comput. 185 (2005) 51-78
43. Schechter S., Krishnan M., and Smith M.D. Using path profiles to predict HTTP requests. In: Enslow P.H., and Ellis A. (Eds). Proceedings of the Seventh International Conference on World Wide Web 7 (Brisbane, Australia) (1998), Elsevier Science Publishers B.V., Amsterdam, The Netherlands 457-467
44. Willard D. New data structures for orthogonal range queries'. SIAM J. Comput. 14 (1985) 232-253
45. S.S.C. Wong, S. Pal, Mining fuzzy association rules for web access case adaptation, in: Workshop on Soft Computing in Case-Based Reasoning, International Conference on Case-Based Reasoning (ICCBR'Ol), 2001.
46. Xiao Y., and Dunham M. Efficient mining of traversal patterns. Data Knowledge Eng. 39 2 (2001) 191-214
47. Xie Y., and Phoha V.V. Web user clustering from access log using belief function. Proc. of the First Inter. Conf. on Knowledge Capture (K-CAP 2001) (2001), ACM Press 202-208
48. A. Ypma, T. Heskes, Clustering web surfers with mixtures of hidden markov models, in: Proceedings of the 14th Belgian-Dutch Conference on AI (BNAIC'02), 2002.
49. O.R. Zaiane, Resource and Knowledge Discovery from the Internet and Multimedia Repositories, Ph.D. Thesis, 1999.
50. Q. Zhao, S.S. Bhowmick, L. Gruenwald, WAM-Miner: in the search of web access motifs from historical web log data, in: Proc. of the 14th ACM CIKM'05, 2005, pp. 421-428.
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