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Authors: Veronica Oliveira de Carvalho 1 ; Daniel Savoia Biondi 1 ; Fabiano Fernandes dos Santos 2 and Solange Oliveira Rezende 2

Affiliations: 1 UNESP - Univ Estadual Paulista, Brazil ; 2 Universidade de São Paulo, Brazil

ISBN: 978-989-8565-10-5

Keyword(s): Association Rules, Post-processing, Clustering, Labeling Methods.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Although association mining has been highlighted in the last years, the huge number of rules that are generated hamper its use. To overcome this problem, many post-processing approaches were suggested, such as clustering, which organizes the rules in groups that contain, somehow, similar knowledge. Nevertheless, clustering can aid the user only if good descriptors be associated with each group. This is a relevant issue, since the labels will provide to the user a view of the topics to be explored, helping to guide its search. This is interesting, for example, when the user doesn’t have, a priori, an idea where to start. Thus, the analysis of different labeling methods for association rule clustering is important. Considering the exposed arguments, this paper analyzes some labeling methods through two measures that are proposed. One of them, Precision, measures how much the methods can find labels that represent as accurately as possible the rules contained in its group and Repetition Frequency determines how the labels are distributed along the clusters. As a result, it was possible to identify the methods and the domain organizations with the best performances that can be applied in clusters of association rules. (More)

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Paper citation in several formats:
Oliveira de Carvalho, V.; Savoia Biondi, D.; Fernandes dos Santos, F. and Oliveira Rezende, S. (2012). Labeling Methods for Association Rule Clustering.In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8565-10-5, pages 105-111. DOI: 10.5220/0003970001050111

author={Veronica Oliveira de Carvalho. and Daniel Savoia Biondi. and Fabiano Fernandes dos Santos. and Solange Oliveira Rezende.},
title={Labeling Methods for Association Rule Clustering},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},


JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Labeling Methods for Association Rule Clustering
SN - 978-989-8565-10-5
AU - Oliveira de Carvalho, V.
AU - Savoia Biondi, D.
AU - Fernandes dos Santos, F.
AU - Oliveira Rezende, S.
PY - 2012
SP - 105
EP - 111
DO - 10.5220/0003970001050111

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