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Estimating the Number of Clusters as a Pre-processing Step to Unsupervised Learning

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

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Abstract

A great challenge in machine learning, as far as unsupervised algorithms are concerned, is to devise methods for pre-estimating the number of clusters associated to a given set of patterns to be clustered. By doing so and by using the number of clusters as input to clustering algorithms that require the information, the chances of getting better results increase substantially. The work described in this paper investigates the performance of an algorithm, based on the sequential clustering BSAS (Basic Sequential Algorithmic Scheme), to produce an ordered list (by frequency of occurrences), containing good estimates for the number of clusters in a given set of patterns. The BSAS is a convenient choice since the order in which patterns are presented to the algorithm can impact the induced clustering. The results of the experiments in eight sets of patterns can be considered empirical evidence that the procedure can be a practical and reliable option, as a pre-processing step, to using clustering algorithms that require the number of clusters.

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Acknowledgments

Authors thank CAPES, CNPq and FACCAMP.

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Authors and Affiliations

  1. Faculdade Campo Limpo Paulista (FACCAMP), Campo Limpo Paulista, São Paulo, Brazil

    Paulo Rogerio Nietto & Maria do Carmo Nicoletti

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

    Maria do Carmo Nicoletti

Authors
  1. Paulo Rogerio Nietto
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  2. Maria do Carmo Nicoletti
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Corresponding author

Correspondence to Paulo Rogerio Nietto .

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Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Nietto, P.R., do Carmo Nicoletti, M. (2017). Estimating the Number of Clusters as a Pre-processing Step to Unsupervised Learning. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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