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Favoring the k-Means Algorithm with Initialization Methods

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 940))

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Abstract

Clustering algorithms are non-supervised algorithms and, among the many available, the k-Means can be considered one of the most popular and successful. The performance of the k-Means, however, is highly dependent on a ‘good’ initialization of the k group centers (centroids) as well as of the value assigned to the number (k) of groups the final clustering should have. This chapter addresses experiments using five initialization algorithms available in the literature namely, the Method1, the k-Means++, the CCIA, the Maedeh&Suresh and the SPSS algorithms, to empirically evaluate their contribution to improving k-Means performance.

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Acknowledgments

The authors thank UNIFACCAMP and CNPq for their support. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior − Brasil (CAPES) − Finance Code 001.

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Authors and Affiliations

  1. Centro Universitário C. Limpo Paulista (UNIFACCAMP), Campo Limpo Paulista, SP, Brazil

    Anderson Francisco de Oliveira & Maria do Carmo Nicoletti

  2. Universidade Federal de S. Carlos (UFSCar), São Carlos, SP, Brazil

    Maria do Carmo Nicoletti

Authors
  1. Anderson Francisco de Oliveira
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  2. Maria do Carmo Nicoletti
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Corresponding author

Correspondence to Maria do Carmo Nicoletti .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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de Oliveira, A.F., do Carmo Nicoletti, M. (2020). Favoring the k-Means Algorithm with Initialization Methods. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_3

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