Rating the Interest of Rules Induced from Data and within Texts

Authors:
Yves Kodratoff
Affiliations:
-
Venue:
DEXA '01 Proceedings of the 12th International Workshop on Database and Expert Systems Applications
Year:
2001

Citing 0
Cited 2

Discovery of Ordinal Association Rules

PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining generalized association rules on biomedical literature

IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: This paper presents an application based on an evaluation of the interestingness of the rules induced from examples using Inductive Text Mining (ITM). The better-known deductive text mining is called Information Extraction, and amounts to finding instances of a predefined pattern in a set of texts. ITM looks for unknown patterns or rules to discover inside a set of texts. We mainly discuss two of the problems of ITM: building ontologies of concepts, and extracting patterns.