Clustering for Visual Analogue Scale Data in Symbolic Data Analysis

Abstract

We propose a hierarchical clustering for the visual analogue scale (VAS) in the framework of Symbolic Data Analysis(SDA). The VAS is a method that can be readily understood by most people to measure a characteristic or attitude that cannot be directly measured. VAS is of most value when looking at change within people, and is of less value for comparing across a group of people because they have different sense. It could be argued that a VAS is trying to produce interval/ratio data out of subjective values that are at best ordinal. Thus, some caution is required in handling VAS. We describe VAS as distribution and handle it as new type data in SDA.In this paper, we define “VAS distribution” as new type data in SDA and propose a hierarchical clustering for this new type data.