DocumentCode :
3425780
Title :
Local mining of Association Rules with Rule Schemas
Author :
Olaru, Andrei ; Marinica, Claudia ; Guillet, Fabrice
Author_Institution :
LINA Lab., Ecole Polytech. de l´´Univ. de Nantes, Nantes
fYear :
2009
fDate :
March 30 2009-April 2 2009
Firstpage :
118
Lastpage :
124
Abstract :
One of the central problems in knowledge discovery in databases, more precisely in the field of association rule mining, relies on the very large number of rules that classic rule mining systems extract. This problem is usually solved by means of a post-processing step, that filters the entire volume of extracted rules, in order to output only a few potentially interesting ones. This article presents a new approach that allows the user to explore the rule space locally, incrementally, without the need to extract and post-process all rules in the database. This solution is based on rule schemas, a new formalism designed in order to improve the representation of user beliefs and expectations, and on a novel algorithm for local association rule mining starting from Schemas. The proposed algorithm has been successfully tested on the database provided by Nantes Habitat.
Keywords :
data mining; database management systems; association rule mining; database management system; knowledge discovery; rule schema; Algorithm design and analysis; Association rules; Data mining; Databases; Filters; Itemsets; Laboratories; Ontologies; Space exploration; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2765-9
Type :
conf
DOI :
10.1109/CIDM.2009.4938638
Filename :
4938638
Link To Document :
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