Performance of Eight Dissimilarity Coefficients to Cluster a Compositional Data Set

  • Maria Cristina Martín
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Concerned with the problem of clustering a compositional data set consisting of vectors of positive components subject to a unit-sum constraint, as a first step we looked for an appropriate dissimilarity coefficient or distance between two compositions. In this paper we selected eight different dissimilarity measures, and their performance was evaluated by means of graphics and cluster validity coefficients of six clustering methods applied to three compositional data sets. Almost recent criteria for measures of compositional difference are also tested for those measures emerging as the best to cluster compositions.


Compositional Data Single Linkage Dissimilarity Measure Ternary Diagram Cluster Validity 
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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • Maria Cristina Martín
    • 1
  1. 1.Faculty of Engineering SciencesOsaka UniversityOsaka 560Japan

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