Database Support for Data Mining Applications: Discovering Knowledge with Inductive QueriesRosa Meo, Pier L. Lanzi, Mika Klemettinen Springer, 2004 M07 28 - 332 pages Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries. |
From inside the book
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... candidates for inductive databases. For instance, considering the prototypical case of assoc1 This research is partially funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme (cInQ project IST-2000 ...
... candidate generation phase that prevents to consider candidates that do not satisfy the monotonic constraint (see, e.g., [18]). The succinctness property that has been introduced in [64] are syntactic constraints that can be put under ...
... candidate itemsets because their frequencies can be inferred from the frequencies of others. However, to be efficient, these algorithms need that such logical rules hold in the data. Let us now consider the δ-free itemsets and how they ...
... candidate generation. In Proceedings ACM SIGMOD'00, pages 1–12, Dallas, Texas, USA, May 2000. ACM Press. H. Hirsh. Theoretical underpinnings of version spaces. In Proceedings IJCAI'91, pages 665–670, Sydney, Australia, Aug. 1991. Morgan ...
... candidates for inductive databases. Most proposals emphasize one of the different phases of the KDD process. This paper 1 Project (IST 2000-26469) partially funded by the ECIST Programme - FET. R. Meo et al. (Eds.): Database Support for ...
Contents
1 | |
24 | |
Declarative Data Mining Using SQL3 | 52 |
Towards a Logic Query Language for Data Mining | 76 |
A Data Mining Query Language for Knowledge Discovery in | 95 |
Towards Query Evaluation in Inductive Databases Using Version Spaces | 117 |
The GUHA Method Data Preprocessing and Mining | 135 |
Constraint Based Mining of First Order Sequences in SeqLog | 154 |
Frequent Itemset Discovery with SQL Using Universal Quantification | 194 |
Deducing Bounds on the Support of Itemsets | 214 |
ModelIndependent Bounding of the Supports of Boolean Formulae | 234 |
Condensed Representations for Sets of Mining Queries | 250 |
Arnaud Giacometti Dominique Laurent Cheikh Talibouya Diop | 270 |
Evgueni N Smirnov Ida G SprinkhuizenKuyper H Japp van den Herik | 289 |
Kimmo Hätönen Mika Klemettinen | 304 |
Artur Bykowski Thomas Daurel Nicolas Méger Christophe Rigotti | 324 |