Symbolic Hierarchical Clustering for Visual Analogue Scale Data

We propose a hierarchical clustering in the framework of Symbolic Data Analysis(SDA). SDA was proposed by Diday at the end of the 1980s and is a new approach for analysing huge and complex data. In SDA, an observation is described by not only numerical values but also “higher-level units”; sets, intervals, distributions, etc. Most SDA works have dealt with only intervals as the descriptions. In this paper, we define “pain distribution” as new type data in SDA and propose a hierarchical clustering for this new type data.