Semantically enhanced clustering in retail using possibilistic K-modes

Abstract

Possibility theory can be used to translate numeric values into semantically more meaningful representation with the help of linguistic variables. The data mining applied to a dataset with linguistic variables can lead to results that are easily interpretable due to the inherent semantics in the representation. Moreover, the data mining algorithms based on these linguistic variables tend to orient themselves based on underlying semantics. This paper describes how to transform a realworld dataset consisting of numeric values using linguistic variables based on possibilistic variables. The transformed dataset is clustered using a recently proposed possibilistic k-modes algorithm. The resulting cluster profiles are semantically accessible with very little numerical analysis.