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

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

The fresh and novel study contributions amassed during this booklet are prolonged and
reworked types of a variety of the simplest 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 laptop technological know-how on the interface among facts and data; corresponding to information Mining, wisdom Discovery, enterprise Intelligence, wisdom Engineering and Semantic internet.

This booklet is meant to be learn through all researchers drawn to those fields, including
PhD or MSc scholars, and researchers from public or deepest laboratories. It
concerns either theoretical and functional elements of KDM.

This booklet 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 booklet, entitled “Ontology and Semantic”, is related
to knowledge-based and user-centered methods in KDM.

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Mining Sequential Patterns: A Context-Aware Approach 37 Runtime. We measure the execution time required to mine context-dependent patterns in a given context, and compare it to the time required to mine frequent sequences in the same context. com, in order to study the vocabulary used according to reviews. This dataset is a subset of the one used in [Jindal and Liu, 2008]. Reviews have been lemmatized1 and grammatically filtered in order to remove uninteresting terms, by using the tree tagger tool [Schmid, 1994].

Fig. 2 The context hierarchy H 30 J. Rabatel, S. Bringay, and P. Poncelet Let us now consider the tuples u =≺id, s, d1 , . . dn of CD according to contexts defined above. The context c = [d1 , . . dn ] is called the context of u. Note that the context of u is minimal (∀i with 1 ≤ i ≤ n, di ∈ dom(Di )). Let u be a tuple in CD and c the context of u. For all contexts c such that c ≥ c we say that c contains u (and u is contained by c ). Let c be a context (not necessarily minimal) in CD. The sequence database of c, denoted by D(c), is the set of tuples contained by c.

Dn of CD according to contexts defined above. The context c = [d1 , . . dn ] is called the context of u. Note that the context of u is minimal (∀i with 1 ≤ i ≤ n, di ∈ dom(Di )). Let u be a tuple in CD and c the context of u. For all contexts c such that c ≥ c we say that c contains u (and u is contained by c ). Let c be a context (not necessarily minimal) in CD. The sequence database of c, denoted by D(c), is the set of tuples contained by c. , |c| = |D(c)|. Example 4. In Table 1, let us consider contexts [o, m] and [o, ∗].

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