Database Support for Data Mining Applications: Discovering by Jean-François Boulicaut (auth.), Rosa Meo, Pier Luca Lanzi,

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 facts, net information, and medical databases housing organic, linguistic, and sensor facts has lately turn into a well-liked manner of learning hidden knowledge.

This publication on database aid for facts mining is constructed to methods exploiting the to be had database know-how, declarative info mining, clever querying, and linked matters, comparable to optimization, indexing, question processing, languages, and constraints. recognition is usually paid to the answer of knowledge preprocessing difficulties, corresponding to information cleansing, discretization, and sampling.

The sixteen reviewed complete papers offered have been rigorously chosen from quite a few workshops and meetings to supply whole and powerfuble insurance of the middle matters. a few papers have been constructed inside of an EC funded undertaking on getting to know wisdom with inductive queries.

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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 facts, net information, and medical databases housing organic, linguistic, and sensor facts has lately turn into a well-liked manner of learning hidden knowledge.

This publication on database aid for facts mining is constructed to methods exploiting the to be had database know-how, declarative info mining, clever querying, and linked matters, comparable to optimization, indexing, question processing, languages, and constraints. recognition is usually paid to the answer of knowledge preprocessing difficulties, corresponding to information cleansing, discretization, and sampling.

The sixteen reviewed complete papers offered have been rigorously chosen from quite a few workshops and meetings to supply whole and powerfuble insurance of the middle matters. a few papers have been constructed inside of an EC funded undertaking on getting to know wisdom with inductive queries.

Show description

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ACM Press. 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.

24–51, 2004. c Springer-Verlag Berlin Heidelberg 2004 Query Languages Supporting Descriptive Rule Mining 25 is a critical evaluation of three proposals in the light of the IDBs’ requirements: MSQL [6,7], DMQL [10,11] and MINE RULE [12,13]. In the paper we discuss also OLE DB for Data Mining (OLE DB DM) by Microsoft and Predictive Model Markup Language (PMML) by Data Mining Group [18]. OLE DB DM is an Application Programming Interface whose aim is to ease the task of developing data mining applications over databases.

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. In other words, that there are no elements of the body of the rule that are not satisfied by tuples of the same original transaction.

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