By Jean-François Boulicaut (auth.), Rosa Meo, Pier Luca Lanzi, Mika Klemettinen (eds.)

Data mining from conventional relational databases in addition to from non-traditional ones reminiscent of semi-structured information, net information, and medical databases housing organic, linguistic, and sensor info has lately develop into a favored means of learning hidden knowledge.

This ebook on database aid for facts mining is constructed to methods exploiting the to be had database know-how, declarative facts mining, clever querying, and linked concerns, corresponding to optimization, indexing, question processing, languages, and constraints. consciousness is additionally paid to the answer of information preprocessing difficulties, similar to info cleansing, discretization, and sampling.

The sixteen reviewed complete papers awarded have been conscientiously chosen from numerous workshops and meetings to supply whole and efficient insurance of the middle concerns. a few papers have been built inside of an EC funded venture on studying wisdom with inductive queries.

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51. L. V. Lakshmanan, R. Ng, J. Han, and A. Pang. Optimization of constrained frequent set queries with 2-variable constraints. In Proceedings SIGMOD’99, pages 157–168, Philadelphia, USA, 1999. ACM Press. 52. S. D. Lee and L. de Raedt. Constraint-based mining of first order sequences in SEQLOG. In Proceedings KDID’02 co-located with ECML-PKDD’02, Helsinki, FIN, Aug. 2002. An extended version appears in this volume. 53. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining.

As rule components (bodies and heads) are stored in relational tables, we use an SQL query to manipulate itemsets. item))) This query is hard to write and to understand. It aims at selecting tuples of the original SalesV iew relation, renamed S1, such that there are no rules with ski pants in the antecedent, that hold on them. These properties are verified by the first two nested SELECT clauses. Furthermore, we want to be sure that the above rules are satisfied by tuples belonging to the same transaction of the original tuple in S1.

3 DMQL DMQL has been designed at the Simon Fraser University, Canada [10,11]. In DMQL, an association rule is a relation between the values of two sets of predicates that are evaluated on the relations of the database. These predicates are of the form P (X, c) where P is a predicate that takes the name of an attribute of the underlying relation, X is a variable and c is a constant value belonging to the attribute’s domain. The predicate is satisfied if in the relation there exists a tuple identified by the variable X whose homonymous attribute takes the value c.

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