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Database Support for Data Mining Applications

Discovering Knowledge with Inductive Queries

  • Book
  • © 2004

Overview

Editors:
  1. Rosa Meo

    1. Dipartimento di Informatica, Università di Torino, Italy

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  2. Pier Luca Lanzi

    1. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy

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    You can also search for this editor in PubMed   Google Scholar

  3. Mika Klemettinen

    1. Nokia Research Center, Nokia Group, Finland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2682)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (16 chapters)

  1. Front Matter

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  2. Database Languages and Query Execution

    1. Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach

      • Jean-François Boulicaut
      Pages 1-23

    2. Query Languages Supporting Descriptive Rule Mining: A Comparative Study

      • Marco Botta, Jean-François Boulicaut, Cyrille Masson, Rosa Meo
      Pages 24-51

    3. Declarative Data Mining Using SQL3

      • Hasan M. Jamil
      Pages 52-75

    4. Towards a Logic Query Language for Data Mining

      • Fosca Giannotti, Giuseppe Manco, Franco Turini
      Pages 76-94

    5. A Data Mining Query Language for Knowledge Discovery in a Geographical Information System

      • Donato Malerba, Annalisa Appice, Michelangelo Ceci
      Pages 95-116

    6. Towards Query Evaluation in Inductive Databases Using Version Spaces

      • Luc De Raedt
      Pages 117-134

    7. The GUHA Method, Data Preprocessing and Mining

      • Petr Hájek, Jan Rauch, David Coufal, Tomáš Feglar
      Pages 135-153

    8. Constraint Based Mining of First Order Sequences in SeqLog

      • Sau Dan Lee, Luc De Raedt
      Pages 154-173

  3. Support for KDD-Process

    1. Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS

      • Matthias Gimbel, Michael Klein, P. C. Lockemann
      Pages 174-193

    2. Frequent Itemset Discovery with SQL Using Universal Quantification

      • Ralf Rantzau
      Pages 194-213

    3. Deducing Bounds on the Support of Itemsets

      • Toon Calders
      Pages 214-233

    4. Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data

      • Artur Bykowski, Jouni K. Seppänen, Jaakko Hollmén
      Pages 234-249

    5. Condensed Representations for Sets of Mining Queries

      • Arnaud Giacometti, Dominique Laurent, Cheikh Talibouya Diop
      Pages 250-269

    6. One-Sided Instance-Based Boundary Sets

      • Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, H Jaap van den Herik
      Pages 270-288

    7. Domain Structures in Filtering Irrelevant Frequent Patterns

      • Kimmo Hätönen, Mika Klemettinen
      Pages 289-305

    8. Integrity Constraints over Association Rules

      • Artur Bykowski, Thomas Daurel, Nicolas Méger, Christophe Rigotti
      Pages 306-323

  4. Back Matter

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Keywords

About this book

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 data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling.

The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.

Editors and Affiliations

  • Dipartimento di Informatica, Università di Torino, Italy

    Rosa Meo

  • Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy

    Pier Luca Lanzi

  • Nokia Research Center, Nokia Group, Finland

    Mika Klemettinen

Bibliographic Information

  • Book Title: Database Support for Data Mining Applications

  • Book Subtitle: Discovering Knowledge with Inductive Queries

  • Editors: Rosa Meo, Pier Luca Lanzi, Mika Klemettinen

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/b99016

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2004

  • Softcover ISBN: 978-3-540-22479-2Published: 28 July 2004

  • eBook ISBN: 978-3-540-44497-8Published: 28 July 2004

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XII, 332

  • Topics: Artificial Intelligence, Database Management, Information Storage and Retrieval

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