Data Mining and Knowledge Discovery

, Volume 13, Issue 2, pp 167–192

A systematic approach to the assessment of fuzzy association rules

Article

DOI: 10.1007/s10618-005-0032-4

Cite this article as:
Dubois, D., Hüllermeier, E. & Prade, H. Data Min Knowl Disc (2006) 13: 167. doi:10.1007/s10618-005-0032-4

Abstract

In order to allow for the analysis of data sets including numerical attributes, several generalizations of association rule mining based on fuzzy sets have been proposed in the literature. While the formal specification of fuzzy associations is more or less straightforward, the assessment of such rules by means of appropriate quality measures is less obvious. Particularly, it assumes an understanding of the semantic meaning of a fuzzy rule. This aspect has been ignored by most existing proposals, which must therefore be considered as ad-hoc to some extent. In this paper, we develop a systematic approach to the assessment of fuzzy association rules. To this end, we proceed from the idea of partitioning the data stored in a database into examples of a given rule, counterexamples, and irrelevant data. Evaluation measures are then derived from the cardinalities of the corresponding subsets. The problem of finding a proper partition has a rather obvious solution for standard association rules but becomes less trivial in the fuzzy case. Our results not only provide a sound justification for commonly used measures but also suggest a means for constructing meaningful alternatives.

Keywords

Association rulesFuzzy setsQuality measuresFuzzy partition

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.IRIT-UPS118 Route de NarbonneToulouse CedexFrance
  2. 2.Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany