Invited Papers

• 
  Arno Siebes: Data Mining in Inductive Databases. 1-23
• 
  Carlo Zaniolo: Mining Databases and Data Streams with Query Languages and Rules. 24-37

Contributed Papers

• 
  Maurizio Atzori, Paolo Mancarella, Franco Turini: Memory-Aware Frequent k-Itemset Mining. 38-54
• 
  Jérémy Besson, Ruggero G. Pensa, Céline Robardet, Jean-François Boulicaut: Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data. 55-71
• 
  Hendrik Blockeel: Experiment Databases: A Novel Methodology for Experimental Research. 72-85
• 
  Toon Calders, Bart Goethals: Quick Inclusion-Exclusion. 86-103
• 
  Tao-Yuan Jen, Dominique Laurent, Nicolas Spyratos, Oumar Sy: Towards Mining Frequent Queries in Star Schemes. 104-123
• 
  Stefan Kramer, Volker Aufschild, Andreas Hapfelmeier, Alexander Jarasch, Kristina Kessler, Stefan Reckow, Jörg Wicker, Lothar Richter: Inductive Databases in the Relational Model: The Data as the Bridge. 124-138
• 
  Taneli Mielikäinen: Transaction Databases, Frequent Itemsets, and Their Condensed Representations. 139-164
• 
  Siegfried Nijssen, Joost N. Kok: Multi-class Correlated Pattern Mining. 165-187
• 
  Csaba István Sidló, András Lukács: Shaping SQL-Based Frequent Pattern Mining Algorithms. 188-201
• 
  Arnaud Soulet, Bruno Crémilleux: Exploiting Virtual Patterns for Automatically Pruning the Search Space. 202-221
• 
  Jan Struyf, Saso Dzeroski: Constraint Based Induction of Multi-objective Regression Trees. 222-233
• 
  Bernard Zenko, Saso Dzeroski, Jan Struyf: Learning Predictive Clustering Rules. 234-250

last updated on Sat May 18 05:21:00 2013 by the dblp team
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