Constraint-Based Mining and Inductive Databases: European by Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De

By Roberto J. Bayardo (auth.), Jean-François Boulicaut, Luc De Raedt, Heikki Mannila (eds.)

The 18 articles during this cutting-edge survey current the most recent leads to inductive querying and constraint-based facts mining and likewise determine destiny instructions of this newly rising box mendacity on the intersection of information mining and database study. The papers handle topical sections on foundations of inductive database frameworks, optimizing inductive queries on neighborhood styles, optimizing inductive queries on international styles, and purposes of inductive querying techniques.

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Additional resources for Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers

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Intelligent Data Analysis Journal, 6(4):341–357, 2002. 37. S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in hiv data. In Proceedings of ACM SIGKDD’01. 38. L. V. S. Lakshmanan, R. T. Ng, J. Han, and A. Pang. Optimization of constrained frequent set queries with 2-variable constraints. SIGMOD Record, 28(2), 1999. 39. W. Li, J. Han, and J. Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. In In Proceedings of ICDM’01. 40. B. Liu, W. Hsu, and Y.

5) The minimum-frequency predicate as defined above is antimonotone with regard to the specialization-relation , since for any pattern g χ s it holds that σD,χ (g) ≥ σD,χ (s). Considering the framework introduced by Mannila and Toivonen [18], the theory of a database D with regard to a matching notion χ and a minimum frequency α can be defined as: Th(L, D, (α, χ)) = {p ∈ L | σD,χ (p) ≥ α} As a consequence of Theorem 1, the set of frequent trees with regard to a data set D for tree inclusion is smaller than that for tree embedding, which is in turn smaller than that for tree incorporation.

Unfortunately, they require information (the frequent itemsets of cardinality k) that is available only at the end of the actual counting (when all transactions have been used). count in the pseudo-code, lines 9 and 14). 3 Convertible Constraints In [48, 49] the class of convertible constraints is introduced, and an FP-growth based methodology to push such constraints is proposed. A constraint CCAM is convertible anti-monotone provided there is an order R on items such that whenever an itemset X satisfies CCAM , so does any prefix of X.

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