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 70
... logic programming, research into data mining from knowledge bases has been almost non-existent, because contemporary methods place the emphasis on the scalability and efficiency of algorithmic solutions, whose inherent procedurality is ...
... Logic Query Language for Data Mining presents a logic database language with elementary data mining mechanisms, such as user-defined aggregates that provide a model, powerful and general as well ... logical and statistical foundations. Its.
... logical and statistical foundations. Its main principle is to let the computer generate and evaluate all the hypotheses that may be interesting given the available data and the domain problem. This work discusses also the relationships ...
... Logic Query Language for Data Mining .................... 76 Fosca Giannotti, Giuseppe Manco, Franco Turini A Data Mining Query Language for Knowledge Discovery in a Geographical Information System ................................... 95 ...
... logical rule. These evaluation functions that return sets of transactions or boolean values are useful when crossing over the patterns and the transactional data. Also, the size of the supporting set is often used. Definition 7 ...
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 |