Summary
K-means clustering is an efficient non-hierarchical clustering method, which became widely used in data mining. In applying the method, however, one needs to specify k,the number of clusters, a priori. In this short paper, we propose an exploratory procedure for setting k using Euclidean and/or Mahalanobis inter-point distances.
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© 2002 The Institute of Statistical Mathematics
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Huh, MH. (2002). Setting the Number of Clusters in K-Means Clustering. In: Baba, Y., Hayter, A.J., Kanefuji, K., Kuriki, S. (eds) Recent Advances in Statistical Research and Data Analysis. Springer, Tokyo. https://doi.org/10.1007/978-4-431-68544-9_5
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DOI: https://doi.org/10.1007/978-4-431-68544-9_5
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-68546-3
Online ISBN: 978-4-431-68544-9
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