Database Support for Data Mining Applications Discovering Knowledge with Inductive Queries

Database Support for Data Mining Applications Discovering Knowledge with Inductive Queries / edited by Rosa Meo, Pier L. Lanzi, Mika Klemettinen.

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...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Meo, Rosa (Editor), Lanzi, Pier L. (Editor), Klemettinen, Mika (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.
Edition:1st ed. 2004.
Series:Lecture Notes in Computer Science, 2682
Springer eBook Collection.
Subjects:
Artificial intelligence.
Database management.
Information storage and retrieval.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
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.

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