By Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad Mobasher, Brij Masand, Philip Yu
This ebook constitutes the completely refereed post-proceedings of the seventh overseas Workshop on Mining net information, WEBKDD 2005, held in Chicago, IL, united states in August 2005 along with the eleventh ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2005. The 9 revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been conscientiously chosen for inclusion within the book.
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Additional 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,
The cost of generalization should be significantly higher than the cost of specialization. 2. Specificity cost property. The cost of traversing edges should be lower when nodes are more specific. 3. Specialization cost property. Further specialization reduces similarity. 44 V. Schickel-Zuber and B. Faltings The generalization cost property models the asymmetry of the similarity function, which implies that the similarity function is not a metric. The specificity cost property represents the fact that sub-concepts are more meaningful to the user than superconcepts, whilst the specialization property reflects the fact that the further away two concepts are, then the more dissimilar they become.
There are hundreds of movies to choose from. For several reasons, this is a difficult problem. First, most people have limited knowledge about the alternatives. Second, the set of alternatives changes frequently. Third, this is an example of a low user involvement decision process, where the user is not prepared to spend hours expressing his preferences. Recommender systems, RS, have been devised as tools to help people in such situations. Two kinds of techniques are widely used in e-commerce sites today.
Using and Learning Semantics in Frequent Subgraph Mining 31 For mining, each graph in the dataset of transactions D is replaced by its conceptual transaction structure. Frequent subgraphs are deﬁned as in Def. 1, with I replaced by C and li by lp . This preprocessing step may alter the graph topology of the input data and thus determines which subgraphs can be (AP-)frequent. It uses semantics to describe which base concept graphs exist. In contrast, AP-IP mining does not alter graph topology: It groups (individual) subgraphs that are isomorphic and that map to the same abstract and frequent subgraph.
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