Database Support for Data Mining Applications: Discovering Knowledge with Inductive Queries

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Rosa 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.

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Contents

Database Languages and Query Execution
1
A Comparative
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

Support for KDDProcess
174

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