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
Results 1-5 of 60
... Frequent Itemsets: The cInQ Approach which presents the main contributions of theoretical and applied nature, in the field of inductive databases obtained in the cInQ project. In Query Languages Supporting Descriptive Rule Mining: A ...
... frequent itemsets discovery. Deducing Bounds on the Support of Itemsets provides a complete set of rules for deducing tight bounds on the support of an itemset if the support of all its subsets are known. These bounds can be used by the ...
... Frequent Itemset Discovery with SQL Using Universal Quantification .... 194 Ralf Rantzau Deducing Bounds on the Support of Itemsets ......................... 214 Toon Calders Model-Independent Bounding of the Supports of Boolean ...
... Frequent Patterns ............ 289 Kimmo Hätönen, Mika Klemettinen Integrity Constraints over Association Rules .......................... 306 Artur Bykowski, Thomas Daurel, Nicolas Méger ... Frequent Itemsets: The cInQ XII Table of Contents.
... Frequent Itemsets: The cInQ Approach Jean-François Boulicaut Institut National des Sciences Appliquées de Lyon, LIRIS CNRS FRE 2672, Bâtiment Blaise Pascal F-69621 Villeurbanne ... Frequent Itemsets: the cInQ Approach Jean-François Boulicaut.
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 |