Relational peculiarity-oriented mining
Data Mining and Knowledge Discovery
Local peculiarity factor and its application in outlier detection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Some issues about outlier detection in rough set theory
Expert Systems with Applications: An International Journal
Explanation oriented association mining using rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A hybrid approach to outlier detection based on boundary region
Pattern Recognition Letters
A survey of outlier detection methodologies and their applications
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Spiral multi-aspect hepatitis data mining
AM'03 Proceedings of the Second international conference on Active Mining
Peculiarity oriented fMRI brain data analysis for studying human multi-perception mechanism
Cognitive Systems Research
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In order to discover new, surprising, interesting patterns hidden in data, peculiarity oriented mining and multi-database mining are required. In the paper, we introduce peculiarity rules as new class of rules, which can be discovered from relatively low number of peculiar data by searching the relevance among the peculiar data. We give formal interpretation and comparison of three classes of rules: association rules, exception rules, and peculiarityrules, as well as describe how to mine more interesting peculiarity rules in multiple databases.