Download Advances in Web Mining and Web Usage Analysis: 7th by Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad PDF
By Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu
This publication constitutes the completely refereed post-proceedings of the seventh overseas Workshop on Mining net facts, WEBKDD 2005, held in Chicago, IL, united states in August 2005 along side the eleventh ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2005. The 9 revised complete papers awarded including an in depth preface went via rounds of reviewing and development and have been rigorously 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,
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.
2 Results Basic statistics. The log is an example of concepts corresponding to a high number of individual URLs (84 concepts with, on average, 82 individual URLs), and highly diverse behaviour at the individual level. 05, the values for K = 6 were: 104 APs, 297466 IPs. This search was not extended because no further (semantically) interesting patterns were found. Fig. 5. 9%). In addition, patterns with 3 or 2 Graph partitioning is simple for log data: The raw data are scanned, and each label li is replaced by bc(li ).
In Proc. 2nd Semantic Web Mining Workshop at PKDD’01. 8. , & Piwowarski, B. (2005). Deducing a term taxonomy from term similarities. In Proc. Knowledge Discovery and Ontologies Workshop at PKDD’05 (pp. 11–22). 9. , & Varlamis, I. (2003). Sewep: Using site semantics and a taxonomy to enhance the web personalization process. In Proc. SIGKDD’03 (pp. 99–108). 10. R. (2003). Large scale mining of molecular fragments with wildcards. In Advances in Intelligent Data Analysis V. (pp. 380–389). 11. , & Prins, J.