On dissimilarity measures at the fuzzy partition level

Abstract

On the one hand, a user vocabulary is often used by soft-computing-based approaches to generate a linguistic and subjective description of numerical and categorical data. On the other hand, knowledge extraction strategies (as e.g. association rules discovery or cluster-ing) may be applied to help the user understand the inner structure of the data. To apply knowledge extraction techniques on subjective and linguistic rewritings of the data, one first has to address the question of defining a dedicated distance metric. Many knowledge extraction techniques indeed rely on the use of a distance metric, whose properties have a strong impact on the relevance of the extracted knowledge. In this paper, we propose a measure that computes the dissimilarity between two items rewritten according to a user vocabulary.