Database support for data mining applications : discovering knowledge with inductive queries

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