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Cluster Computing
Erschienen in: Cluster Computing 6/2019

02.03.2018

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

verfasst von: Shruti Aggarwal, Paramvir Singh

Erschienen in: Cluster Computing | Sonderheft 6/2019

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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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Metadaten
Titel
Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms
verfasst von
Shruti Aggarwal
Paramvir Singh
Publikationsdatum
02.03.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 6/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2262-4

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