TY - GEN
T1 - Discovery of surprising exception rules based on intensity of implication
AU - Suzuki, Einoshin
AU - Kodratoff, Yves
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.
PY - 1998
Y1 - 1998
N2 - This paper presents an algorithm for discovering surprising exception rules from data sets. An exception rule, which is defined as a deviational pattern to a common sense, exhibits unexpectedness and is sometimes extremely useful. A domain-independent approach, PEDRE, exists for the simultaneous discovery of exception rules and their common sense rules. However, PEDRE, being too conservative, have difficulty in discovering surprising rules. Historic exception discoveries show that surprise is often linked with interestingness. In order to formalize this notion we propose a novel approach by improving PEDRE. First, we reformalize the problem and settle a looser constraints on the reliability of an exception rule. Then, in order to screen out uninteresting rules, we introduce, for an exception rule, an evaluation criterion of surprise by modifying intensity of implication, which is based on significance. Our approach has been validated using data sets from the UCI repository.
AB - This paper presents an algorithm for discovering surprising exception rules from data sets. An exception rule, which is defined as a deviational pattern to a common sense, exhibits unexpectedness and is sometimes extremely useful. A domain-independent approach, PEDRE, exists for the simultaneous discovery of exception rules and their common sense rules. However, PEDRE, being too conservative, have difficulty in discovering surprising rules. Historic exception discoveries show that surprise is often linked with interestingness. In order to formalize this notion we propose a novel approach by improving PEDRE. First, we reformalize the problem and settle a looser constraints on the reliability of an exception rule. Then, in order to screen out uninteresting rules, we introduce, for an exception rule, an evaluation criterion of surprise by modifying intensity of implication, which is based on significance. Our approach has been validated using data sets from the UCI repository.
UR - http://www.scopus.com/inward/record.url?scp=84947703686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84947703686&partnerID=8YFLogxK
U2 - 10.1007/bfb0094800
DO - 10.1007/bfb0094800
M3 - Conference contribution
AN - SCOPUS:84947703686
SN - 3540650687
SN - 9783540650683
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 10
EP - 18
BT - Principles of Data Mining and Knowledge Discovery - 2nd European Symposium, PKDD 1998, Proceedings
A2 - Zytkow, Jan M.
A2 - Quafafou, Mohamed
PB - Springer Verlag
T2 - 2nd European Symposium on Principles of Data Mining and Knowledge Discovery in Databases, PKDD 1998
Y2 - 23 September 1998 through 26 September 1998
ER -