Download PDF by Dominique Gay, Marc Boullé (auth.), Fabrice Guillet, Bruno: Advances in Knowledge Discovery and Management

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 contemporary and novel learn contributions gathered during this booklet are prolonged and
reworked models of a range of the simplest papers that have been initially provided 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 capacity "Knowledge Discovery and administration" or KDM.
KDM is worried with the works in machine technological know-how on the interface among information and data; reminiscent of facts Mining, wisdom Discovery, enterprise Intelligence, wisdom Engineering and Semantic internet.

This ebook is meant to be learn by way of all researchers attracted to those fields, including
PhD or MSc scholars, and researchers from public or inner most laboratories. It
concerns either theoretical and sensible points of KDM.

This publication has been dependent in elements.
The first half, entitled “Data Mining, type 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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Extra resources for Advances in Knowledge Discovery and Management

Example text

Mining sequential patterns by pattern-growth: the PrefixSpan approach. : Multidimensional sequential pattern mining. In: Proceedings of the Tenth International Conference on Information and Knowledge Management. : M2 SP: Mining Sequential Patterns Among Several Dimensions. , Gama, J. ) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 205–216. : An algorithm for suffix stripping. : Contextual Sequential Pattern Mining. In: 2010 IEEE International Conference on Data Mining Workshops, pp. 981– 988. : Probabilistic part-of-speech tagging using decision trees.

More precisely, the following is organized as follows. In Section 2, we define the “traditional” sequential pattern mining problem and show why it is not relevant when contextual information is available. Contextual data, as well as contextdependent sequential patterns, are presented in Section 3. In Section 4, we highlight some relevant properties of context-dependent sequential patterns that are exploited to propose an efficient algorithm. In Section 5, conducted experiments on a realworld dataset are presented.

In Section 2, we define the “traditional” sequential pattern mining problem and show why it is not relevant when contextual information is available. Contextual data, as well as contextdependent sequential patterns, are presented in Section 3. In Section 4, we highlight some relevant properties of context-dependent sequential patterns that are exploited to propose an efficient algorithm. In Section 5, conducted experiments on a realworld dataset are presented. We conclude and discuss future work in Section 6.

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Advances in Knowledge Discovery and Management by Dominique Gay, Marc Boullé (auth.), Fabrice Guillet, Bruno Pinaud, Gilles Venturini, Djamel Abdelkader Zighed (eds.)


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