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Data Analysis, Machine Learning and Knowledge Discovery pp 41–49Cite as

How Many Bee Species? A Case Study in Determining the Number of Clusters

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Abstract

It is argued that the determination of the best number of clusters k is crucially dependent on the aim of clustering. Existing supposedly “objective” methods of estimating k ignore this. k can be determined by listing a number of requirements for a good clustering in the given application and finding a k that fulfils them all. The approach is illustrated by application to the problem of finding the number of species in a data set of Australasian tetragonula bees. Requirements here include two new statistics formalising the largest within-cluster gap and cluster separation. Due to the typical nature of expert knowledge, it is difficult to make requirements precise, and a number of subjective decisions is involved.

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Author information

Authors and Affiliations

  1. Department of Statistical Science, University College London, Gower St, London, WC1E 6BT, UK

    Christian Hennig

Authors
  1. Christian Hennig
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Correspondence to Christian Hennig .

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Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

  2. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

    Lars Schmidt-Thieme

  3. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

    Ruth Janning

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Hennig, C. (2014). How Many Bee Species? A Case Study in Determining the Number of Clusters. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_5

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