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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... considering the pattern domain of itemsets. Mining itemsets has been proved useful not only for association rule mining but also feature construction, classification, clustering, etc. We introduce the concepts of pattern domain ...
... consider the pattern domain of itemsets and databases that are transactional databases. Doing so, we can provide examples of concepts that have emerged as important within the cInQ project after 18 months of work. It is useful to ...
... consider that: – A so-called transactional database contains the data, – Patterns are the so-called itemsets and one kind of descriptive rule that can be derived from them, i.e., the association rules. Definition 1 (Transactional ...
... consider now several evaluation functions that have been less studied in the data mining context but have been proved quite useful in the last 3 years (see, e.g., [65,12,75,15,16]). Notice however that these concepts have been Inductive ...
... consider examples of primitive constraints that can be built from them. 2.3 Primitive Constraints Many primitive constraints can be defined. We consider some examples that have been proved useful. These examples are representative of ...
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 |