Web user clustering and Web prefetching using Random Indexing with weight functions

Knowledge and Information Systems

Volumn 33, Issue 1, 2012, Pages 89-115

  Wan, Miao ^a,b   Jönsson, Arne ^a   Wang, Cong ^b   Li, Lixiang ^b   Yang, Yixian ^b  

^a LINKÖPING UNIVERSITY   (Sweden)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Random Indexing; Web prefetching; Web user clustering; Weight functions

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; INDEXING (OF INFORMATION); VECTOR SPACES; WEBSITES;

COMMON USER PROFILE; INTRINSIC CHARACTERISTICS; NAVIGATIONAL PATTERNS; RANDOM INDEXING; VECTOR SPACE MODELS; WEB PRE-FETCHING; WEB USER CLUSTERING; WEIGHT FUNCTIONS;

ELECTRONIC DOCUMENT EXCHANGE;

EID: 84867097182     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-011-0453-x     Document Type: Article

References (49)

1. Anderson CR (2002) Amachine learning approach to web personalization. Ph. D. thesis, University of Washington.
2. Ansari S, Kohavi R, Mason L, Zheng Z (2000) Integrating e-commerce and data mining: architecture and challenges. In: Proceedings of the 2001 IEEE international conference on data mining (ICDM 2001), pp 27-34.
3. Berendt B (2002) Using site semantics to analyze, visualize, and support navigation. Data Min Knowl Discov 6(1): 37-59.
4. Berry MJA, Linoff G (1996) Data mining techniques for marketing, sales and customer support. Wiley, London.
5. Bezerra BLD, de Assis Tenório de Carvalho F (2010) Symbolic data analysis tools for recommendation systems. Knowl Inf Syst (on-line).
6. Bundschus M, Yu Sh, Tresp V, Rettinger A, Dejori M, Kriegel H-P (2009) Hierarchical bayesian models for collaborative tagging systems. In: Proceedings IEEE international conference on data mining (ICDM 2009), pp 728-733.
7. Cadez I, Heckerman D, Meek C, Smyth P, Whire S (2002) Visualization of navigation patterns on a website using model based clustering. Technical Report MSR-TR-00-18, Microsoft Research.
8. Catledge LD, Pitkow JE (1995) Characterizing browsing strategies in the world-wide web. Comput Netw ISDN Syst 27: 1065-1073.
9. Characteristics of WWW Client Traces, Cunha CA, Bestavros A, Crovella ME (1995) Boston University Department of Computer Science. Technical Report TR-95-010. http://ita. ee. lbl. gov/html/contrib/BU-Web-Client. html.
10. Chatterjee N, Mohan S (2008) Discovering word senses from text using Random Indexing. In: Gelbukh A (ed) Computational linguistics and intelligent text processing (Lecture Notes in Computer Science), CICLing 2008, LNCS 4919, pp 299-310.
11. Cooley R (2000) Web usage mining: discovery and application of interesting patterns from web data. Ph. D. thesis, University of Minnesota.
12. Cooley R, Mobasher B, Srivastava J (1999) Data preparation for mining world wide web browsing patterns. J Knowl Inf Syst 1(1): 5-32.
13. Curran JR (2004) From distributional to semantic similarity. Ph. D. thesis, University of Edinburgh.
14. Etzioni O (1996) The world-wide Web: quagmire or gold mine. Commun ACM 39(11): 65-68.
15. Feng S, Wang D, Yu G, Gao W, Wong K (2010) Extracting common emotions from blogs based on fine-grained sentiment clustering. Knowl Inf Syst 24(1). doi: 10. 1007/s10115-010-0325-9.
16. Fu Y, Creado M, Ju C (2001) Reorganizing web sites based on user access patterns. In: Proceedings of the tenth international conference on information and knowledge management, pp 583-585.
17. Gorman J, Curran JR (2006) Random Indexing using statistical weight functions. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP 2006), Sydney, Australia, pp 457-464.
18. Halkidi M, Vazirgiannis M, Batistakis I (2000) Quality scheme assessment in the clustering process. In: Proceedings of the 4th European conference on principles and Practice of Knowledge Discovery in Databases (PKDD 2000), Lyon, France.
19. Hou J, Zhang Y (2002) Constructing good quality web page communities. In: Proceedings of the 13th Australasian database conferences (ADC2002), vol 36. ACS Inc, Melbourne, pp 65-74.
20. Hou J, Zhang Y (2003) Effectively finding relevant web pages from linkage information. IEEE Trans Knowl Data Eng 15(4): 940-951.
21. IBM (2003) SurfAid analytics. http://surfaid. dfw. ibm. com.
22. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall.
23. Jin X, Zhou Y, Mobasher B (2004) A unified approach to personalization based on probabilistic latent semantic models of web usage and content. In: Proceedings of the AAAI 2004 workshop on semantic web personalization (SWP'04), San Jose.
24. Kanerva P, Kristofersson J, Holst A (2000) Random Indexing of text samples for latent semantic analysis. In: Proceedings of the 22nd annual conference of the cognitive science society. Erlbaum, New Jersey, p 1036.
25. Kanerva P (1988) Sparse distributed memory. The MIT Press, Cambridge.
26. Kanerva P, Sjödin G, Kristofersson J, Karlsson R, Levin B, Holst A, Karlgren J, Sahlgren M (2001) Computing with large random patterns. In: Uesaka Y, Kanerva P, Asoh H (eds) Foundations of real-world intelligence. CSLI Publications, Stanford.
27. Kaski S (1999) Dimensionality reduction by random mapping: fast similarity computation for clustering. In Proceedings of the international joint conference on neural networks (IJCNN98), IEEE Service Center.
28. Krishnapuram R, Joshi A, Nasraoui O, Yi L (2003) Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Trans Fuzzy Syst 4(9): 596-607.
29. Lan B, Bressan S, Ooi BC, Tan K (2000) Rule-assisted prefetching in web server caching. In: Proceedings of 2000 ACM international conference on information and knowledge management (Virginia, USA), vol 1. ACM, New York, pp 504-11.
30. Landauer T, Dumais S (1997) A solution to Platos problem: the latent semantic analysis theory for acquisition, induction and representation of knowledge. Psychol Rev 104(2): 211-240.
31. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, pp 281-297.
32. Mobasher B, Cooley R, Srivastava J (2000) Automatic personalization based on web usage mining. Commun ACM 8(43): 142-151.
33. Nasraoui O, Frugui H, Krishnapuram R, Joshi A (2000) Extracting web user profiles using relational competitive fuzzy clustering. Int J Artif Intell Tools 4(9): 509-526.
34. Nanopoulos A, Katsaros D, Manolopoulos Y (2001) Effective prediction of web-user accesses: a data mining approach. In Proceedings of workshop web usage analysis and user profiling (WebKDD'01) (San Francisco, USA). ACM, New York.
35. Oceans Research Group. Department of Computer Science, Boston University. http://cs-www. bu. edu/groups/oceans/Home. html.
36. Paliouras G, Papatheodorou C, Karkaletsis V, Spyropoulos CD (2000) Clustering the users of large web sites into communities. In: Proceedings of the international conference on machine learning (ICML), pp 719-726.
37. Pal SK, Ghosh A, Uma Shankar B (2000) Segmentation of remotely sensed images with fuzzy thresholding and quantitative evaluation. Int J Remote Sens 21(11): 2269-2300.
38. Sahlgren M, Karlgren J (2005) Automatic bilingual lexicon acquisition using Random Indexing of parallel corpora. J Nat Lang Eng (Special Issue on Parallel Texts).
39. Sahlgren M, Karlgren J (2005) Automatic bilingual lexicon acquisition using Random Indexing of parallel corpora. J Nat Lang Eng Special Issue Parallel Texts 11(3): 1-14.
40. Theodoridis S, Koutroumbas K (2006) Pattern recognition, 3rd edn. Academic Press, New York.
41. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson Addison-Wesley, Reading.
42. The Internet Traffic Archive. http://ita. ee. lbl. gov/index. html.
43. Teng W, Chang C, Chen M (2005) Integrating web caching and web prefetching in client-side proxies. IEEE Trans Parallel Distrib Syst 16: 444-455.
44. Tian W, Choi B, Phoha VV (2002) An adaptive web cache access predictor using neural network. In: Proceedings of 15th international conference on IEA/AIE (Cairns, Australia), vol 2358. Springer, Berlin, pp 450-459.
45. Wan M, Li L, Xiao J, Yang Y, Wang C, Guo X (2010) CAS based clustering algorithm for Web users. Nonlinear Dyn 61(3): 347-361.
46. Wu Y, Chen A (2002) Prediction of web page accesses by proxy server log. World Wide Web 5: 67-88.
47. Xie Y, Phoha VV (2001) Web user clustering from access log using belief function. In: Proceedings of the 1st international conference on Knowledge capture, pp 202-208.
48. Yang S, Li Y, Wu X, Pan R (2006) Optimization study on k value of K-means algorithm. J Syst Simul 18(3): 97-101.
49. Zhou Y, Jin X, Mobasher B (2004) A recommendation model based on latent principal factors in web navigation data. In: Proceedings of the 3rd international workshop on web dynamics. ACM Press, New York.