By Rosine Cicchetti, Lotfi Lakhal, Sébastien Nedjar (auth.), Fabrice Guillet, Gilbert Ritschard, Djamel Abdelkader Zighed (eds.)
During the decade, wisdom Discovery and administration (KDM or, in French, EGC for Extraction et Gestion des connaissances) has been a radical and fruitful learn subject within the French-speaking medical neighborhood. In 2003, this enthusiasm for KDM resulted in the basis of a selected French-speaking organization, referred to as EGC, devoted to aiding and selling this subject. extra accurately, KDM is anxious with the interface among wisdom and knowledge akin to, between different issues, info Mining, wisdom Discovery, company Intelligence, wisdom Engineering and Semantic net. the hot and novel study contributions accrued during this ebook are prolonged and transformed types of a range of the easiest papers that have been initially awarded in French on the EGC 2010 convention held in Tunis, Tunisia in January 2010.
The quantity is geared up in 3 components. half I comprises 4 chapters serious about quite a few features of knowledge dice and Ontology-based representations. half II consists of 4 chapters serious about effective trend Mining matters, whereas partly III the final 4 chapters deal with info Preprocessing and data Retrieval.
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Additional resources for Advances in Knowledge Discovery and Management: Volume 2
Wöß, W. ) DaWaK 2004. LNCS, vol. 3181, pp. 391–400. : Cube lattices: A framework for multidimensional data mining. , Kamath, C. ) SDM. : Extracting semantics from data cubes using cube transversals and closures. , Faloutsos, C. ) KDD, pp. 69–78. : Convex Cube: Towards a Unified Structure for Multidimensional Databases. , Pernul, G. ) DEXA 2007. LNCS, vol. 4653, pp. 572–581. : Closed cube lattices. : Lossless reduction of datacubes using partitions. : Mining border descriptions of emerging patterns from dataset pairs.
Fi/u/jomuhone/ 42 H. Brahmi et al. 02 Support Threshold (%) (f) Retail Fig. 3 Mining time of the CND-Cube using the C LOSE NDMG and FIRM algorithms Moreover, the C LOSE NDMG algorithm is more efficient on sparse datasets for all the MinSup values. The difference between the performances of C LOSE NDMG and FIRM reaches its maximal for the R ETAIL dataset. For these sparse datasets, C LOSE NDMG is on average 31 times faster than FIRM. , it is a non-reduced one, generated using the “CUBE” operator as illustrated in Figure 1 for the relation example “Car-Sale”.
Interestingly enough, the obtained rates for sparse contexts outperform those obtained by the other representations for the same datasets. Considering the three concise representations Closed Cube, Quotient Cube and CND-Cube, we conclude that the best compression rates of a full data cube are 44 H. Brahmi et al. , R ETAIL and T10I4D100K datasets. 3 A Case Study To test our approach, we used a set of real data on “Car-Sales”. The data are gathered from a database of an international private company, called “Le Moteur”(9 ) , specialized in the distribution of motor vehicles.
Advances in Knowledge Discovery and Management: Volume 2 by Rosine Cicchetti, Lotfi Lakhal, Sébastien Nedjar (auth.), Fabrice Guillet, Gilbert Ritschard, Djamel Abdelkader Zighed (eds.)