Issue:Temporal aspects of web log analysis

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http://ifigenia.org/wiki/issue:nifs/9/4/114-122

Title of paper: Temporal aspects of web log analysis Author(s): Ilias Petrounias Department of Computation, UMIST, PO Box 88, Manchester M60 1QD, UK A. Assaid Department of Computation, UMIST, PO Box 88, Manchester M60 1QD, UK Panagiotis Chountas Department of Computer Science, University of Westminster, Northwick Watford Rd, Northwick Park, London, HA] 3TP, UK Boyan Kolev Centre for Biomedical Engineering, Bulgarian Academy of Sciences, Acad.G.Bonchev Str., BI.105, Sofia-1113, BULGARIA Presented at: Seventh International Conference on IFSs, Sofia, 23-24 August 2003 Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 9 (2003) Number 4, pages 114-122 Download:  PDF (5163  Kb, File info) Abstract: This paper is concerned with mining temporal features from web logs. We present two methods. The first one concerns the temporal mining of sequential patterns in which we use sequence data which are used as support for discovered patterns in order to find periodicity in web log data. The second one concerns an efficient method for finding periodicity in web log sequence data which handles missing sequences by dealing with the overlap problem. References: Chaudhuri, S., U., Dayal, (1997). An overview of Data warehousing and OLAP technology, ACM SIGMOD. 26:65-74. Demiriz, A, M. J., Zaki (2002). webSPADE: A Parallel Sequence Mining Algorithm to Analyze Web Log Data. SIGKDD °02 Edmonton, Alberta CA. Han, J., W., Gong, and Y., Yin, (1998). Mining segment-wise periodic patterns in time-related database. Proceedings of knowledge discovery and Data Mining (KDD'98). Zaki, M.J. Efficient Enumeration of Frequent Sequences. 7th International Conference on Information and Knowledge Management. Washington DC, pp. 68-75. M.J., Zaki, (2000). Sequence Mining in Categorical Domains: Incorporating Constraints, in 9th International Conference on Information and Knowledge Management Washington, DC., pp. 422-429. M.J., Zaki (2001). SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning Journal, special issue on Unsupervised Learning (Doug Fisher, ed.). Vol. 42 Nos. 1/2: 31-60. Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.