×

zbMATH — the first resource for mathematics

Incremental web usage mining based on active ant colony clustering. (English) Zbl 1115.68321
Summary: To alleviate the scalability problem caused by the increasing Web using and changing users’ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and “flying over the plane” moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users’ interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.
MSC:
68M10 Network design and communication in computer systems
Software:
AntClust
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Kosala R. Web Mining Research: A Survey [J].ACM SIGKDD Explorations, 2000,2(1):1–15. · Zbl 05442805 · doi:10.1145/360402.360406
[2] Facca F M. Recent Developments in Web Usage Mining Research [C]//5th International Conference on Data Warehousing and Knowledge Discovery, Prague, Czeth Republic, Sept 2003:140–150.
[3] Abraham A, Web Usage Mining Using Artificial Ant Colony Clustering and Linear Genetic Programming [J].CEC, 2003,2:1384–1391.
[4] Labroche N, MonmarchĂ© N, Venturini G. AntClust: Ant Clustering and Web Usage Mining [C]//GECCO Conference, Chicago, 2003. · Zbl 1028.68819
[5] Masseglia F, Poncelet P, Teisseire M. Incremental Mining of Sequential Patterns in Large Databases [J].Data & Knowledge Engineering, 2003,46(1):97–121. · doi:10.1016/S0169-023X(02)00209-4
[6] Woon Y K, Ng W K, Lim E P. Online and Incremental Mining of Separately-grouped Web Access Logs [C]//WISE 2002. New York: IEEE Comput Soc, 2002:53–62.
[7] Yang Y, Kamel M. Clustering Ensemble Using Swarm Intelligence [C]//IEEE Swarm Intelligence Symposium. Piscataway, NJ: IEEE Service Center, 2003:65–71.
[8] Chen Ling, Xu Xiaohua, Chen Yixin. A Novel Ant Clustering Algorithm Based on Cellular Automata [C]//Proc of the IAT. Beijing, September 20–24, 2004: 148–154.
[9] Ayad H, Kamel M. Topic Discovery from Text Using Aggregation of Different Clustering Methods [C]//Proc of the AI02. Calgary, 2002:161–175. · Zbl 1048.68576
[10] Lumer E, Faieta B. Diversity and Adaptation in Populations of Clustering Ants [C]//Proc of the SAB94, Boston: MIT Press, 1994:499–508.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.