Database Support for Data Mining Applications: Discovering Knowledge with Inductive QueriesAuthor: Rosa Meo, Pier Luca Lanzi, Mika Klemettinen Published by Springer Berlin Heidelberg ISBN: 978-3-540-22479-2 DOI: 10.1007/b99016Table 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