Database Support for Data Mining Applications Discovering Knowledge with Inductive Queries /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Meo, Rosa (Editor, http://id.loc.gov/vocabulary/relators/edt), Lanzi, Pier L. (Editor, http://id.loc.gov/vocabulary/relators/edt), Klemettinen, Mika (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.
• Edition: 1st ed. 2004.
• Series: Lecture Notes in Computer Science, 2682
• Subjects: Artificial intelligence.; Database management.; Information storage and retrieval.; Artificial Intelligence.; Database Management.; Information Storage and Retrieval.

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...

Table of Contents:

• Database Languages and Query Execution
• 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
• Support for KDD-Process
• 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.