Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms

Abstract

Traditional k-means clustering algorithm is sensitive to the choice of initial cluster centers and leads to local optimal results. k-means++ is a hybrid k-means clustering algorithm which specifies the procedure to initialize the cluster centers before proceeding with the standard k-means algorithm. Inspired by nature, some contemporary optimization techniques such as Cuckoo, Bat and Krill Herd algorithms etc., are used for optimization as they mimic the swarming behaviour and allows to cooperatively move towards an optimal objective within a reasonable time. In this paper, these nature-inspired techniques are used for optimizing k-means++ clustering algorithm to enhance clustering quality and generate new hybrids of unprecedented performance. The results of the evaluation experiments on the integration of nature-inspired optimization methods with k-means++ algorithm are reported.

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Correspondence to Paramvir Singh.

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Aggarwal, S., Singh, P. Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms. Cluster Comput 22, 14169–14180 (2019). https://doi.org/10.1007/s10586-018-2262-4

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Keywords

  • k-means
  • k-means++
  • Nature inspired algorithms
  • Swarm based techniques
  • Cuckoo search algorithm
  • Bat algorithm
  • Krill Herd algorithm