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
... sequences, such that they are directly available to programmers and ready to be called by applications. Therefore, it is envisioned that we should be able now to mine relational databases for interesting rules directly from database ...
... Sequences in SeqLog presents a logical language, SeqLog, for mining and querying sequential data and databases. This language is used as a representation language for an inductive database system. In this system, variants of level-wise ...
... Sequences in SeqLog ........... 154 Sau Dan Lee, Luc De Raedt II Support for KDD-Process Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS................................................. 174 Matthias ...
... sequence of queries over the data but also the so-called theory of the data. Given a language L of patterns (e.g., itemsets, sequences, association rules), the theory of a database r with respect to L and a selection predicate q is the ...
... sequences of queries until he/she gets an actionable collection of patterns. So, we have not only to optimize single inductive query evaluations but also the evaluation of sequences of queries. This has been studied for itemsets and ...
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 |