By Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu
This booklet constitutes the completely refereed post-proceedings of the seventh overseas Workshop on Mining internet facts, WEBKDD 2005, held in Chicago, IL, united states in August 2005 at the side of the eleventh ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2005. The 9 revised complete papers provided including a close preface went via rounds of reviewing and development and have been conscientiously chosen for inclusion within the book.
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Extra info for Advances in Web Mining and Web Usage Analysis: 7th International Workshop on Knowledge Discovery on the Web, WEBKDD 2005, Chicago, IL, USA, August 21,
2. g. ), under variation of the number of transactions/sessions, the branching factor b of the concepts in a one-level taxonomy (and thus the number of patterns found in a total of 100 “URLs”), the diversity of patterns (each node had a parameterized transition probability pS to one other, randomly chosen node and an equal distribution of transition probabilities to all other nodes), and the length of patterns (the transition probability pL to “exit”—average session length becomes 1/pL, and pattern length increases with it).
Results of graph clustering. Figure 3 reveals a very high number of individual patterns, an observation that was commonly made in these data: Individual patterns with frequency = 1 constituted 99% of all patterns. The reasons are the very large database behind the Web site and the highly idiosyncratic information needs of its users; they make the request for each data record (and for each set of data records) very infrequent. com) top-level group it referred to. Query mining that based the feature-space representation of patterns on URLs was used for two reasons that also apply in many other Web-server log analyses: First, to understand what visitors were looking for rather than what they obtained (often, empty result sets), and second, because URLs were much more amenable to semantic analysis than their corresponding Web pages, which often consisted of pictorial material with very heterogeneous characteristics.
2001). Visualizing Individual Diﬀerences in Web Navigation: STRATDYN, a Tool for Analyzing Navigation Patterns. BRMIC, 33, 243–257. 3. , & Stumme, G. (2004). Usage mining for and on the semantic web. In H. Kargupta et al. ), Data Mining: Next Generation Challenges and Future Directions (pp. 461–480). Menlo Park, CA: AAAI/MIT Press. 4. , & Kralisch, A. (2005). Analysing and visualising logﬁles: the Individualised SiteMap tool ISM. In Proc. GOR05. 5. , & Spiliopoulou, M. (2000). Analysis of navigation behaviour in web sites integrating multiple information systems.
Advances in Web Mining and Web Usage Analysis: 7th International Workshop on Knowledge Discovery on the Web, WEBKDD 2005, Chicago, IL, USA, August 21, by Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu