Abstract is missing.
- Data Mining in Inductive DatabasesArno Siebes. 1-23 [doi]
- Mining Databases and Data Streams with Query Languages and RulesCarlo Zaniolo. 24-37 [doi]
- Memory-Aware Frequent ::::k::::-Itemset MiningMaurizio Atzori, Paolo Mancarella, Franco Turini. 38-54 [doi]
- Constraint-Based Mining of Fault-Tolerant Patterns from Boolean DataJérémy Besson, Ruggero G. Pensa, Céline Robardet, Jean-François Boulicaut. 55-71 [doi]
- Experiment Databases: A Novel Methodology for Experimental ResearchHendrik Blockeel. 72-85 [doi]
- Quick Inclusion-ExclusionToon Calders, Bart Goethals. 86-103 [doi]
- Towards Mining Frequent Queries in Star SchemesTao-Yuan Jen, Dominique Laurent, Nicolas Spyratos, Oumar Sy. 104-123 [doi]
- Inductive Databases in the Relational Model: The Data as the BridgeStefan Kramer, Volker Aufschild, Andreas Hapfelmeier, Alexander Jarasch, Kristina Kessler, Stefan Reckow, Jörg Wicker, Lothar Richter. 124-138 [doi]
- Transaction Databases, Frequent Itemsets, and Their Condensed RepresentationsTaneli Mielikäinen. 139-164 [doi]
- Multi-class Correlated Pattern MiningSiegfried Nijssen, Joost N. Kok. 165-187 [doi]
- Shaping SQL-Based Frequent Pattern Mining AlgorithmsCsaba István Sidló, András Lukács. 188-201 [doi]
- Exploiting Virtual Patterns for Automatically Pruning the Search SpaceArnaud Soulet, Bruno Crémilleux. 202-221 [doi]
- Constraint Based Induction of Multi-objective Regression TreesJan Struyf, Saso Dzeroski. 222-233 [doi]
- Learning Predictive Clustering RulesBernard Zenko, Saso Dzeroski, Jan Struyf. 234-250 [doi]