Name
Abstract | ||
|---|---|---|
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.
|
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/BFb0094800 | PKDD |
Keywords | Field | DocType |
surprising exception rules,rule based | Information system,Data mining,Common sense,Information processing,Computer science,Artificial intelligence,Surprise,Rule of inference,Knowledge acquisition | Conference |
Volume | ISSN | ISBN |
1510 | 0302-9743 | 3-540-65068-7 |
Citations | PageRank | References |
42 | 2.52 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Einoshin Suzuki | 1 | 853 | 93.41 |
| Yves Kodratoff | 2 | 581 | 172.25 |