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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... attributes it contains. We must then provide evaluation functions that compute these measures. It is straightforward to define the frequency evaluation function of an association rule X ⇒ Y in r as F(X ⇒ Y,r) = F(X ∪ Y,r) [1,2]. When ...
... attributes or at most 1 negative attribute). On different real data sets, it has been possible to get interesting results when it was combined with condensed representations (see Section 4). Mining Condensed Representation of Frequent ...
... attribute, the result-sets of the two queries exhibit an inclusion relationship when a functional dependency is present between the differing attributes. [11] studies also the equivalence properties that two MINE RULE queries present ...
... Attribute, Event Sequence, and Event Type Similarity Notions for Data Mining. PhD thesis, Department of Computer Science, P.O. Box 26, FIN-00014 University of Helsinki, Jan. 2000. B. Nag, P. M. Deshpande, and D. J. DeWitt. Using a ...
... attribute. In practice, MSQL extracts propositional rules like A⇒B, where A is a conjunctset and B is a descriptor ... attributes into discrete values. Notice that during mining, the discretization is done “on the fly”, so that it is ...
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 |