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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... database languages would be necessary. This vision entails that the optimization issues should be addressed at the system level for which we have now a significant body of research, while the analyst could concentrate better on the ...
... database technology and data mining systems, such as OLEDB-DM and PMML. Declarative Data Mining Using SQL-3 shows a new approach, compared to existing SQL approaches, to mine association rules from an object-relational database: it uses ...
... databases. This language is used as a representation language for an inductive database system. In this system, variants of level-wise algorithms for computing the version space of the solutions are proposed and experimented in the user ...
... System ................................... 95 Donato Malerba, Annalisa Appice, Michelangelo Ceci Towards Query Evaluation in Inductive Databases Using Version Spaces . 117 Luc De Raedt The GUHA Method, Data Preprocessing and Mining ...
... databases, will help the system to speed-up the query answering procedures. Again these ideas have been carried over to the logical theory of inductive databases [29]. Partners of the consortium have been inspired by the MINE RULE query ...
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 |