On Careful Selection of Initial Centers for K-means Algorithm

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


K-means clustering algorithm is rich in literature and its success stems from simplicity and computational efficiency. The key limitation of K-means is that its convergence depends on the initial partition. Improper selection of initial centroids may lead to poor results. This paper proposes a method known as Deterministic Initialization using Constrained Recursive Bi-partitioning (DICRB) for the careful selection of initial centers. First, a set of probable centers are identified using recursive binary partitioning. Then, the initial centers for K-means algorithm are determined by applying a graph clustering on the probable centers. Experimental results demonstrate the efficacy and deterministic nature of the proposed method.


Clustering K-means algorithm Initialization Bi-partitioning 


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Copyright information

© Springer India 2016

Authors and Affiliations

  • R. Jothi
    • 1
  • Sraban Kumar Mohanty
    • 1
  • Aparajita Ojha
    • 1
  1. 1.Indian Institute of Information TechnologyDesign and Manufacturing JabalpurJabalpurIndia

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