JUCS - Journal of Universal Computer Science 12(6): 627-653, doi: 10.3217/jucs-012-06-0627
Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table
expand article infoEinoshin Suzuki‡
‡ Kyushu University, Japan

Corresponding author: Einoshin Suzuki ( suzuki@i.kyushu-u.ac.jp )

© Einoshin Suzuki.
This article is freely available under the J.UCS Open Content License.
Citation: Suzuki E (2006) Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table. JUCS - Journal of Universal Computer Science 12(6): 627-653. https://doi.org/10.3217/jucs-012-06-0627
Open Access
Abstract
In this paper, we survey efforts devoted to discovering interesting exceptions from data in data mining. An exception differs from the rest of data and thus is interesting and can be a clue for further discoveries. We classify methods into exception instance discovery, exception rule discovery, and exception structured-rules discovery and give a condensed and comprehensive introduction.
Keywords
exception, instance, rule, interestingness, unexpectedness, pattern, data mining