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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... operator for capturing the functionality of the proposed approach. Towards a Logic Query Language for Data Mining presents a logic database language with elementary data mining mechanisms, such as user-defined aggregates that provide a ...
... operators in a KDD-aware implementation algebra. In this way data mining operators can be integrated smoothly into a database system, thus allowing interactivity, scalability and resource control. This framework is based on the ...
... operators =, =, <, ≤, >, ≥. It says the aggregate of the set of numeric values in S stands in relationship θ to v. Example 4. Consider the database of Figure 1, assume that Csize (S) ≡ |S| ≤ 2 (it is equivalent to count(S) ≤ 2) and ...
... operator have been studied [11,58]. Two important and challenging new notions include: pattern views and relations among inductive queries. Both of these notions have also been included (and were actually inspired on MINE RULE) in the ...
... operator. 5. Conclusions. We provided a presentation of the itemset pattern domain. Any progress on constraint-based mining for itemsets can influence the research on the multiple uses of frequent itemsets (feature construction ...
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 |