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- Publisher
- Springer Journals
- Copyright
- Copyright © 2013 by Springer Science+Business Media Dordrecht
- Subject
- Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Statistical Physics, Dynamical Systems and Complexity
- ISSN
- 1012-2443
- eISSN
- 1573-7470
- DOI
- 10.1007/s10472-013-9370-x
- Publisher site
- See Article on Publisher Site
Abstract
Measures of interestingness play a crucial role in association rule mining. An important methodological problem, on which several papers appeared in the literature, is to provide a reasonable classification of the measures. In this paper, we explore Boolean factor analysis, which uses formal concepts corresponding to classes of measures as factors, for the purpose of clustering of the measures. Unlike the existing studies, our method reveals overlapping clusters of interestingness measures. We argue that the overlap between clusters is a desired feature of natural groupings of measures and that because formal concepts are used as factors in Boolean factor analysis, the resulting clusters have a clear meaning and are easy to interpret. We conduct three case studies on clustering of measures, provide interpretations of the resulting clusters and compare the results to those of the previous approaches reported in the literature.
Journal
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Aug 2, 2013