Advances in Web Mining and Web Usage Analysis: 7th by Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Manshad

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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 internet facts, WEBKDD 2005, held in Chicago, IL, united states in August 2005 together with the eleventh ACM SIGKDD overseas convention on wisdom Discovery and information Mining, KDD 2005. The 9 revised complete papers offered 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,

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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. As a consequence, the specificity property reduces the cost of traversing edges as we go deeper in the ontology and the specialization property increases the cost between two concepts if other concepts are found on the path between these concepts.

In particular, node label and degree illustrate what is present (or not), and the role of items or concepts (an endpoint of a pattern if the degree is 1, or a hub if the degree is high). This leads to a second simplification, a graph code applicable for all types of label functions: the lexicographically sorted pairs of (node label,node degree), plus optionally edge labels. In step 4. viCode need to be computed, there is no test for automorphism. Again, sorting can be done efficiently. 3 Graph Clustering for Finding Individual Patterns The notion of AP-frequent subgraphs rests on differentiating between all possible individual-level instances of an abstract pattern.

Notation I: Mining input (a) a dataset D = {Gd } of graph transactions Gd = (V d , E d , ld ), where each Gd consists of a finite set of nodes V d , a set of edges E d , and labels from a set I given by ld : V d ∪ E d → I; (b) a taxonomy T consisting of concepts from a set C; (c) a mapping to abstract concepts ac : I → C; (d) minsupp ∈ [0, 1]. Definition 1. An abstract subgraph is a connected graph Ga = (V, E, la ) consisting of a finite set of nodes V , a set of edges E, and labels given by la : V ∪ E → C.

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