Journal of Classification

, Volume 32, Issue 3, pp 414–442 | Cite as

Bisecting K-Means and 1D Projection Divisive Clustering: A Unified Framework and Experimental Comparison

  • Ekaterina V. Kovaleva
  • Boris G. Mirkin


The paper presents a least squares framework for divisive clustering. Two popular divisive clustering methods, Bisecting K-Means and Principal Direction Division, appear to be versions of the same least squares approach. The PDD recently has been enhanced with a stopping criterion taking into account the minima of the corresponding one-dimensional density function (dePDDP method). We extend this approach to Bisecting K-Means by projecting the data onto random directions and compare thus modified methods. It appears the dePDDP method is superior at datasets with relatively small numbers of clusters, whatever cluster intermix, whereas our version of Bisecting K-Means is superior at greater cluster numbers with noise entities added to the cluster structure.


Divisive clustering Bisecting k-means Split base decomposition Uphierarchy Principal directions Random directions Computational experiment Cluster structure generator 


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© Classification Society of North America 2015

Authors and Affiliations

  1. 1.Department of Data Analysis and Machine IntelligenceNational Research University Higher School of EconomicsMoscowRussia
  2. 2.Department of Computer Science and Information SystemsBirkbeck University of LondonLondonUK

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