Database Support for Data Mining Applications 2004

• Jean-François Boulicaut:
  Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach. 1-23
  
• Marco Botta, Jean-François Boulicaut, Cyrille Masson, Rosa Meo:
  Query Languages Supporting Descriptive Rule Mining: A Comparative Study. 24-51
  
• Hasan M. Jamil:
  Declarative Data Mining Using SQL3. 52-75
  
• Fosca Giannotti, Giuseppe Manco, Franco Turini:
  Towards a Logic Query Language for Data Mining. 76-94
  
• Donato Malerba, Annalisa Appice, Michelangelo Ceci:
  A Data Mining Query Language for Knowledge Discovery in a Geographical Information System. 95-116
  
• Luc De Raedt:
  Towards Query Evaluation in Inductive Databases Using Version Spaces. 117-134
  
• Petr Hájek, Jan Rauch, David Coufal, Thomas Feglar:
  The GUHA Method, Data Preprocessing and Mining. 135-153
  
• Sau Dan Lee, Luc De Raedt:
  Constraint Based Mining of First Order Sequences in SeqLog. 154-173
  
• Matthias Gimbel, Michael Klein, Peter C. Lockemann:
  Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS. 174-193
  
• Ralf Rantzau:
  Frequent Itemset Discovery with SQL Using Universal Quantification. 194-213
  
• Toon Calders:
  Deducing Bounds on the Support of Itemsets. 214-233
  
• Artur Bykowski, Jouni K. Seppänen, Jaakko Hollmén:
  Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data. 234-249
  
• Arnaud Giacometti, Dominique Laurent, Cheikh Talibouya Diop:
  Condensed Representations for Sets of Mining Queries. 250-269
  
• Evgueni N. Smirnov, Ida G. Sprinkhuizen-Kuyper, H. Jaap van den Herik:
  One-Sided Instance-Based Boundary Sets. 270-288
  
• Kimmo Hätönen, Mika Klemettinen:
  Domain Structures in Filtering Irrelevant Frequent Patterns. 289-305
  
• Artur Bykowski, Thomas Daurel, Nicolas Méger, Christophe Rigotti:
  Integrity Constraints over Association Rules. 306-323