Slice_OP: Selecting Initial Cluster Centers Using Observation Points

  • Md Abdul Masud
  • Joshua Zhexue Huang
  • Ming Zhong
  • Xianghua Fu
  • Mohammad Sultan Mahmud
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)


This paper proposes a new algorithm, Slice_OP, which selects the initial cluster centers on high-dimensional data. A set of observation points is allocated to transform the high-dimensional data into one-dimensional distance data. Multiple Gamma models are built on distance data, which are fitted with the expectation-maximization algorithm. The best-fitted model is selected with the second-order Akaike information criterion. We estimate the candidate initial centers from the objects in each component of the best-fitted model. A cluster tree is built based on the distance matrix of candidate initial centers and the cluster tree is divided into K branches. Objects in each branch are analyzed with k-nearest neighbor algorithm to select initial cluster centers. The experimental results show that the Slice_OP algorithm outperformed the state-of-the-art Kmeans++ algorithm and random center initialization in the k-means algorithm on synthetic and real-world datasets.


Initial cluster center Clustering algorithm Center initialization Observation point 



This paper was supported by National Natural Science Foundations of China (under Grant No. 61473194 and 61472258) and Shenzhen-Hong Kong Technology Cooperation Foundation (under Grant No. SGLH20161209101100926).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Md Abdul Masud
    • 1
  • Joshua Zhexue Huang
    • 1
  • Ming Zhong
    • 1
  • Xianghua Fu
    • 1
  • Mohammad Sultan Mahmud
    • 1
  1. 1.Big Data Institute, College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

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