Clustering for visual analogue scale data in symbolic data analysis

Kotoe Katayama, Yamaguchi Rui, Seiya Imoto, Keiko Matsuura, Kenji Watanabe, Satoru Miyano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProcedia Computer Science
Number of pages5
Publication statusPublished - 2011
EventComplex Adaptive Systems - Chicago, IL, United States
Duration: 2011 Oct 302011 Nov 2


OtherComplex Adaptive Systems
CountryUnited States
CityChicago, IL


  • Distribution valued data
  • Hierarchical clustering

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Katayama, K., Rui, Y., Imoto, S., Matsuura, K., Watanabe, K., & Miyano, S. (2011). Clustering for visual analogue scale data in symbolic data analysis. In Procedia Computer Science (Vol. 6, pp. 370-374)