Subjective Evaluation of Labeling Methods for Association Rule Clustering

• Published in: Advances in Soft Computing and Its Applications pp. 289 - 300
• Main Authors: de Padua, Renan, dos Santos, Fabiano Fernandes, da Silva Conrado, Merley, de Carvalho, Veronica Oliveira, Rezende, Solange Oliveira
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2013
• Series: Lecture Notes in Computer Science
• Subjects: Association Rule; Association Rule Mining; Label Method; Repetition Frequency; Subjective Evaluation
• ISBN: 3642451101; 9783642451102
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-45111-9_26

Among the post-processing association rule approaches, clustering is an interesting one. When an association rule set is clustered, the user is provided with an improved presentation of the mined patters. The domain to be explored is structured aiming to join association rules with similar knowledge. To take advantage of this organization, it is essential that good labels be assigned to the groups, in order to guide the user during the association rule exploration process. Few works have explored and proposed labeling methods for this context. Moreover, these methods have not been explored through subjective evaluations in order to measure their quality; usually, only objective evaluations are used. This paper subjectively evaluates five labeling methods used on association rule clustering. The evaluation aims to find out the methods that presents the best results based on the analysis of the domain experts. The experimental results demonstrate that there is a disagreement between objective and subjective evaluations as reported in other works from literature.