Cluster validity measure and merging system for hierarchical clustering considering outliers

Frank De Morsier, Devis Tuia, Maurice Borgeaud, Volker Gass, Jean Philippe Thiran

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)


Clustering algorithms have evolved to handle more and more complex structures. However, the measures that allow to qualify the quality of such clustering partitions are rare and have been developed only for specific algorithms. In this work, we propose a new cluster validity measure (CVM) to quantify the clustering performance of hierarchical algorithms that handle overlapping clusters of any shape and in the presence of outliers. This work also introduces a cluster merging system (CMS) to group clusters that share outliers. When located in regions of cluster overlap, these outliers may be issued by a mixture of nearby cores. The proposed CVM and CMS are applied to hierarchical extensions of the Support Vector and Gaussian Process Clustering algorithms both in synthetic and real experiments. These results show that the proposed metrics help to select the appropriate level of hierarchy and the appropriate hyperparameters.

Original languageEnglish
Pages (from-to)1478-1489
Number of pages12
JournalPattern Recognition
Issue number4
Publication statusPublished - Apr 2015
Externally publishedYes


  • Agglomerative clustering
  • Clustering
  • Gaussian processes
  • Quality
  • Support vector clustering

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