CAS based clustering algorithm for Web users

Nonlinear Dynamics

Volumn 61, Issue 3, 2010, Pages 347-361

  Wan, Miao ^a   Li, Lixiang ^a   Xiao, Jinghua ^a   Yang, Yixian ^a   Wang, Cong ^a   Guo, Xiaolei ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Chaotic ant swarm (CAS); Clustering; Web access logs; Web user clustering

Indexed keywords

CHAOTIC ANT SWARM; CHAOTIC OPTIMIZATION; CLUSTERING; CLUSTERING METHODS; CLUSTERING PROBLEMS; CLUSTERING QUALITY; CLUSTERING TECHNIQUES; INTRA-CLUSTER; K-MEANS ALGORITHM; MECHANISM ANALYSIS; PREFETCHING; STATISTICAL ANALYSIS; WEB ACCESS LOGS; WEB USER CLUSTERING; WEB USERS;

CLUSTER ANALYSIS; QUALITY CONTROL; WORLD WIDE WEB;

CLUSTERING ALGORITHMS;

EID: 77956616022     PISSN: 0924090X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11071-010-9653-2     Document Type: Article

References (42)

1. Miyano, T., Tsutsui, T.: Data synchronization in a network of coupled phase oscillators. Phys. Rev. Lett. 98, 024102 (2007)
2. Ye, Z., Hu, S., Yu, J.: Adaptive clustering algorithm for community detection in complex networks. Phys. Rev. E 78, 046115 (2008)
3. Feldt, S., Waddell, J., Hetrick, V. L., Berke, J. D., Żochowski, M.: Functional clustering algorithm for the analysis of dynamic network data. Phys. Rev. E 79, 056104 (2009)
4. Reichardt, J., Leone, M.: (Un) detectable cluster structure in sparse networks. Phys. Rev. Lett. 101, 078701 (2008)
5. Gfeller, D., Chappelier, J. C., DeLosRios, P.: Finding instabilities in the community structure of complex networks. Phys. Rev. E 72, 056135 (2005)
6. Newman, M. E. J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)
7. Hu, Y., Li, M., Zhang, P., Fan, Y., Di, Z.: Community detection by signaling on complex networks. Phys. Rev. E 78, 016115 (2008)
8. Vázquez, A., Oliveira, J. G., Dezsö, Z., Goh, K. I., Kondor, I., Barabási, A. L.: Modeling bursts and heavy tails in human dynamics. Phys. Rev. E 73, 036127 (2006)
9. Gonçalves, B., Ramasco, J. J.: Human dynamics revealed through Web analytics. Phys. Rev. E 78, 026123 (2008)
10. Meiss, M. R., Menczer, F., Fortunato, S., Flammini, A., Vespignani, A.: Ranking Web Sites with Real User Traffic. In: Proc. WSDM '08, California, vol. 1, pp. 65-76. ACM, New York (2008)
11. Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43, 142-151 (2000)
12. Paik, H. Y., Benatallah, B., Hamadi, R.: Dynamic restructuring of e-catalog communities based on user interaction patterns. World Wide Web 5, 325-366 (2002)
13. IBM, SurfAid Analytics, http://surfaid.dfw.ibm.com (2003)
14. Padmanabhan, V. N., Mogul, J. C.: Using predictive prefetching to improve world wide web latency. ACM Comput. Commun. Rev. 3, 23-36 (1996)
15. Berendt, B.: Using site semantics to analyze, visualize, and support navigation. Data Mining Knowl. Discov. 6, 37-59 (2002)
16. Fu, Y., Creado, M., Ju, C.: Reorganizing web sites based on user access patterns. In: Proc. 10th Int. Conf. on Information and Knowledge Management, Georgia, USA, vol. 1, pp. 583-585. ACM, New York (2001)
17. Ansari, S., Kohavi, R., Mason, L., Zheng, Z.: Integrating ecommerce and data mining: architecture and challenges. In: Proc. 2001 IEEE Int. Conf. on Data Mining (ICDM 2001), San Mateo, USA, vol. 1, pp. 27-34. IEEE Computer Society, Washington (2000)
18. MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. 5th Berkeley Symp. on Math. Statist. and Prob., Berkeley, vol. 1, pp. 281-297. University of California Press, Berkeley (1967)
19. Aihara, K., Takabe, T., Toyoda, M.: Chaotic neural networks. Phys. Lett. A 144, 333-340 (1990)
20. Chen, L., Aihara, K.: Chaotic simulated annealing by a neural network model with transient chaos. Neural Netw. 8, 915 (1995)
21. Cai, J., Ma, X., Li, L., Peng, H.: Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Convers. Manag. 48, 645 (2007)
22. Tokuda, I., Aihara, K., Nagashima, T.: Adaptive annealing for chaotic optimization. Phys. Rev. E 58, 5157 (1998)
23. Yang, D., Li, G., Cheng, G.: On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34, 1366-1375 (2007)
24. Li, L., Yang, Y., Peng, H., Wang, X.: An optimization method inspired by chaotic ant behavior. Int. J. Bifurc. Chaos 16, 2351-2364 (2006)
25. Li, L., Yang, Y., Peng, H., Wang, X.: Parameters identification of chaotic systems via chaotic ant swarm. Chaos Solitons Fractals 28, 1204-1211 (2006)
26. Cai, J., Ma, X., Li, L., Yang, Y., Peng, H., Wang, X.: Chaotic ant swarm optimization to economic dispatch. Electric Power Syst. Res. 77, 1373-1380 (2007)
27. Li, L., Yang, Y., Peng, H.: Fuzzy system identification via chaotic ant swarm. Chaos Solitons Fractals 40, 1399-1407 (2009)
28. Cole, B. J.: Is animal behavior chaotic? Evidence from the activity of ants. Proc. R. Soc. Lond. B, Biol. Sci. 244, 253-259 (1991)
29. Solé, R. V., Miramontes, O., Goodwill, B. C.: Oscillations and chaos in ant societies. J. Theor. Biol. 161, 343-357 (1993)
30. Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowl. Inf. Syst. 1, 5-32 (1999)
31. Cooley, R.: Web usage mining: discovery and application of interesting patterns from web data. PhD thesis, University of Minnesota (2000)
32. Anderson, C. R.: Amachine learning approach to web personalization. PhD thesis, University of Washington (2002)
33. Catledge, L. D., Pitkow, J. E.: Characterizing browsing strategies in the World-Wide Web. Comput. Netw. ISDN Syst. 27, 1065-1073 (1995)
34. Cunha, C. A., Bestavros, A., Crovella, M. E.: Characteristics of WWW client traces. Boston University Department of Computer Science, Technical Report TR-95-010, April 1995. http://ita.ee.lbl.gov/html/contrib/BU-Web-Client. html
35. The Internet Traffic Archive. http://ita.ee.lbl.gov/index.html
36. Teng, W., Chang, C., Chen, M.: Integrating web caching and web prefetching in client-side proxies. IEEE Trans. Parallel Distrib. Syst. 16, 444-455 (2005)
37. Lan, B., Bressan, S., Ooi, B. C., Tan, K.: Rule-assisted prefetching in web server caching. In: Proc. 2000 ACM Int. Conf. on Information and Knowledge Management, Virginia, USA, vol. 1, pp. 504-511. ACM, New York (2000)
38. Nanopoulos, A., Katsaros, D., Manolopoulos, Y.: Effective prediction of web-user accesses: a data mining approach. In: Proc. Workshop Web Usage Analysis and User Profiling (WebKDD'01), San Francisco, USA. ACM, New York (2001)
39. Pitkow, J., Pirolli, P.: Mining longest repeating subsequence to predict world wide web surfing. In: Proc. 2nd USENIX Symp. Internet Technologies and Systems (USENIX, 1999), Colorado, USA. vol. 1, pp. 139-150
40. Tian, W., Choi, B., Phoha, V. V.: An adaptive web cache access predictor using neural network. In: Proc. 15th Int. Conf. on IEA/AIE, Cairns, Australia, vol. 2358, pp. 450-459. Springer, Berlin (2002)
41. Wu, Y., Chen, A.: Prediction of web page accesses by proxy server log. World Wide Web 5, 67-88 (2002)
42. Mulvenna, M. D., Anand, S. S., Buchner, A. G.: Personalization on the net using web mining. Commun. ACM 43, 123-125 (2000)