By Dominique Gay, Marc Boullé (auth.), Fabrice Guillet, Bruno Pinaud, Gilles Venturini, Djamel Abdelkader Zighed (eds.)
The contemporary and novel study contributions accrued during this ebook are prolonged and
reworked types of a range 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 capacity "Knowledge Discovery and administration" or KDM.
KDM is anxious with the works in machine technological know-how on the interface among information and information; reminiscent of facts Mining, wisdom Discovery, enterprise Intelligence, wisdom Engineering and Semantic net.
This publication is meant to be learn by way of all researchers drawn to those fields, including
PhD or MSc scholars, and researchers from public or inner most laboratories. It
concerns either theoretical and functional elements of KDM.
This publication has been based 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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Additional info for Advances in Knowledge Discovery and Management
C ∈ decomp(c), s is frequent in c , 2. ∀c ∈ M decomp(c), s is not frequent in c . Proof: This result is obtained by directly applying Theorem 1 and Lemma 4 to the definition of a c-exclusive sequential pattern (Definition 3). Hence, a c-exclusive sequential pattern is a c-general sequential pattern s such that there does not exist a minimal context outside the decomposition of c where s is frequent. Hence, Theorems 1 and 2 show that both general and exclusive sequential patterns can be mined by considering minimal contexts only, while naive approaches require to consider all descendants of c to extract c-general sequential patterns, and all the contexts of the hierarchy to mine c-exclusive sequential patterns.
So, the process continues by outputting (a)(b) , and using it as a new prefix. We now present the Gespan algorithm that aims at mining general and exclusive sequential patterns in a context. The prefix-growth approach of PrefixSpan is used to extract general sequential patterns, relying on the anti-monotonicity of the c-generality property. From a prefix sequence s, the algorithm builds the s-projected database by making use of the method BuildPro jectedDatabase, and scans the projected database (method ScanDB) to find items i that can be assembled to form a new general sequential pattern s .
However, an expert 28 J. Rabatel, S. Bringay, and P. Poncelet studying context-dependent patterns in the whole database does not want a pattern being frequent in young customers only to be considered representative in the whole database. 2. A sequential pattern extracted in a given population does not bring any information about the rest of the population. For instance, an expert studying frequent behaviors in the young customers population will extract the sequence s = (a)(b) . However, the only information provided by this sequential pattern is that young customers frequently follow this behavior.