C4.5: programs for machine learning
C4.5: programs for machine learning
Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Discovery of M-of-N Concepts for Classification
DS '00 Proceedings of the Third International Conference on Discovery Science
Undirected exception rule discovery as local pattern detection
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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Extracting interesting rules from databases is an important field of knowledge discovery. Typically, enormous number of rules are embedded in a database and one of the essential abilities of discovery systems is to evaluate interestingness of rules to filter out less interesting rules. This paper proposes a new criterion of rule's interestingness based on its exceptionality. This criterion evaluates exceptionality of rules by comparing their accuracy with those of simpler and more general rules. We also propose a disovery algorithm, DIG, to extract interesting rules with respect to the criterion effectively.