Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Knowledge Discovery in Databases
Knowledge Discovery in Databases
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Exception Rule Mining with a Relative Interestingness Measure
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Efficient Mining of Niches and Set Routines
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
An Efficient Association Rule Mining Algorithm for Classification
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Discovering itemset interactions
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Finding minimal rare itemsets and rare association rules
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Efficiently mining both association and correlation rules
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Mining both associated and correlated patterns
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Efficient mining of dissociation rules
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Efficiently mining mutually and positively correlated patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Mining bridging rules between conceptual clusters
Applied Intelligence
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
BruteSuppression: a size reduction method for Apriori rule sets
Journal of Intelligent Information Systems
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Finding patterns from data sets is a fundamental task of data mining. If we categorize all patterns into strong, weak, and random, conventional data mining techniques are designed only to find strong patterns, which hold for numerous objects and are usually consistent with the expectations of experts. While such strong patterns are helpful in prediction, the unexpectedness and contradiction exhibited by weak patterns are also very useful although they represent a relatively small number of objects. In this paper, we address the problem of finding weak patterns (i.e., reliable exceptions) from databases. A simple and efficient approach is proposed which uses deviation analysis to identify interesting exceptions and explore reliable ones. Besides, it is flexible in handling both subjective and objective exceptions. We demonstrate the effectiveness of the proposed approach through a set of real-life data sets, and present interesting findings.