KDID 2005: Porto, Portugal

Francesco Bonchi, Jean-François Boulicaut (Eds.): Knowledge Discovery in Inductive Databases, 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers. Lecture Notes in Computer Science 3933 Springer 2006, ISBN 3-540-33292-8 BibTeX

Invited Papers

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

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