Ordinal association rules for error identification in data sets

Authors:
Andrian Marcus;Jonathan I. Maletic;King-Ip Lin
Affiliations:
Kent State University, Kent, OH;Kent State University, Kent, OH;The University of Memphis, Memphis, TN
Venue:
Proceedings of the tenth international conference on Information and knowledge management
Year:
2001

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Abstract

A new extension of the Boolean association rules, ordinal association rules, that incorporates ordinal relationships among data items, is introduced. One use for ordinal rules is to identify possible errors in data. A method that finds these rules and identifies potential errors in data is proposed.