Comparison Between k-Means and k-Medoids for Mixed Variables Clustering

  • Norin Rahayu ShamsuddinEmail author
  • Nor Idayu Mahat
Conference paper


This paper compares the performance of k-means and k-medoids in clustering objects with mixed variables. The k-means initially means for clustering objects with continuous variables as it uses Euclidean distance to compute distance between objects. While, k-medoids has been designed suitable for mixed type variables especially with PAM (partition around medoids). By using a mixed variables data set on a modified cancer data, we compared k-means and k-medoids on internal validity set up in R package. The result indicates that k-medoids is a good clustering option when the measured variables are mixed with different types.


Mixed variables k-means k-medoids Silhouette Dunn index 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia
  2. 2.School of Quantitative SciencesCollege of Arts and Sciences, Universiti Utara MalaysiaChanglunMalaysia

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