Database Support for Data Mining Applications: Discovering Knowledge with Inductive Queries Author: Rosa Meo, Pier Luca Lanzi, Mika Klemettinen Published by Springer Berlin Heidelberg ISBN: 978-3-540-22479-2 DOI: 10.1007/b99016
Table of Contents:
Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach
Query Languages Supporting Descriptive Rule Mining: A Comparative Study
Declarative Data Mining Using SQL3
Towards a Logic Query Language for Data Mining
A Data Mining Query Language for Knowledge Discovery in a Geographical Information System
Towards Query Evaluation in Inductive Databases Using Version Spaces
The GUHA Method, Data Preprocessing and Mining
Constraint Based Mining of First Order Sequences in SeqLog
Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS
Frequent Itemset Discovery with SQL Using Universal Quantification
Deducing Bounds on the Support of Itemsets
Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data
Condensed Representations for Sets of Mining Queries
One-Sided Instance-Based Boundary Sets
Domain Structures in Filtering Irrelevant Frequent Patterns
Integrity Constraints over Association Rules
Includes bibliographical references and author index
Database languages and query execution — Support for KDD-process
