By Dominique Gay, Marc Boullé (auth.), Fabrice Guillet, Bruno Pinaud, Gilles Venturini, Djamel Abdelkader Zighed (eds.)

ISBN-10: 3642358543

ISBN-13: 9783642358548

ISBN-10: 3642358551

ISBN-13: 9783642358555

The fresh and novel examine contributions accrued during this publication are prolonged and
reworked types of a range of the easiest papers that have been initially offered in
French on the EGC’2011 convention held in Brest, France, on January 2011.
EGC stands for "Extraction et Gestion des connaissances" in French, and skill "Knowledge Discovery and administration" or KDM.
KDM is worried with the works in machine technology on the interface among information and information; comparable to facts Mining, wisdom Discovery, enterprise Intelligence, wisdom Engineering and Semantic internet.

This e-book is meant to be learn by way of all researchers attracted to those fields, including
PhD or MSc scholars, and researchers from public or deepest laboratories. It
concerns either theoretical and useful facets of KDM.

This ebook has been established in components.
The first half, entitled “Data Mining, class and queries”, bargains with rule and development mining, with topological approaches
and with OLAP.
The moment a part of the publication, entitled “Ontology and Semantic”, is related
to knowledge-based and user-centered methods in KDM.

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Rabatel, S. Bringay, and P. Poncelet 3. Output general sequential patterns in c that are not general in any other context c. This approach is very time consuming, as it requires to mine sequential patterns in all contexts of the hierarchy. However, a similar reasoning as for c-general sequential patterns can be applied to redefine the c-exclusivity property. Lemma 4. There exists a context c c such that s is c -general iff there exists a minimal context c such that c ∈ / decomp(c) and s is frequent in c .

Given the definition of a c-general sequential pattern, a naive approach could be performed in the following steps: 1. Mine sequential patterns in c. 2. Mine sequential patterns in every descendants of c. 3. Output sequential patterns frequent in c and all its descendants. , c and c’s descendants). In order to overcome this drawback, we exploit the properties of the context hierarchy and show that c-general sequential patterns can be extracted by focusing only on the decomposition of c. Theorem 1.

Books, bad, ∗] or [Books, bad, 75−100]) the mining of exclusive sequential patterns is time consuming. This is due to the number of minimal contexts that must be considered in order to test the exclusivity of a sequential pattern. For instance, in order to test whether a general sequential pattern s is exclusive in [Books, bad, 75−100], Gespan needs to check whether s is frequent in one of the 47 other minimal contexts in the hierarchy. This number of minimal contexts is lower when the considered context is more general.

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