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 clinical neighborhood. In 2003, this enthusiasm for KDM resulted in the basis of a particular French-speaking organization, referred to as EGC, devoted to helping and selling this subject. extra accurately, KDM is worried with the interface among wisdom and information equivalent to, between different issues, information Mining, wisdom Discovery, enterprise Intelligence, wisdom Engineering and Semantic net. the new and novel learn contributions accrued during this e-book are prolonged and transformed models of a range of the simplest papers that have been initially offered in French on the EGC 2010 convention held in Tunis, Tunisia in January 2010.
The quantity is geared up in 3 elements. half I comprises 4 chapters thinking about a number of points of information dice and Ontology-based representations. half II consists of 4 chapters involved in effective development Mining concerns, whereas partially III the final 4 chapters tackle info Preprocessing and data Retrieval.
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Additional info for Advances in Knowledge Discovery and Management: Volume 2
Furthermore, the problem worsens since we deal with large datasets. For instance, Ross and Srivastava exemplify the problem by achieving a full data cube encompassing more than 210 million of tuples from an input relation having 1 million of tuples [Ross and Srivastava, 1997]. The huge size of a data cube makes data cube computation time-consuming. Although cheap and highly-sized volume memory chips are available, it is difficult to hold the whole data cube of a large relation in the main memory.
In: Proceedings of the ACM-SIGMOD International Conference on Management of Data, Washington, USA, pp. : Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994), Santiago, Chile, pp. : Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets. J. ) CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. : Enhanced Mining of Association Rules from Data Cubes. In: Proceedings of the 9th ACM International Workshop on Data Warehousing and OLAP, Arlington, Virginia, USA, pp.
2003a], we conclude that the operators ROLLUP and D RILLDOWN can be used in the CND-Cube lattice. Thus, the closed non derivable data cube preserves the semantics of the operators ROLLUP and D RILLDOWN over the data cube lattice, for the aggregate functions C OUNT and S UM. Example 3. The closed non derivable data cube lattice, obtained from our relation “Car-Sale” and illustrated by Figure 2, preserves the semantics of the cube operators ROLLUP and D RILLDOWN for the aggregative function SUM.
Advances in Knowledge Discovery and Management: Volume 2 by Rosine Cicchetti, Lotfi Lakhal, Sébastien Nedjar (auth.), Fabrice Guillet, Gilbert Ritschard, Djamel Abdelkader Zighed (eds.)