Summary
The paper addresses the problem of identifying relevant values for the number of clusters present in a data set. The problem has usually been tackled by searching for a best partition using so-called stopping rules. It is argued that it can be of interest to detect cluster structure at several different levels, and five stopping rules that performed well in a previous investigation are modified for this purpose. The rules are assessed by their performance in the analysis of simulated data sets which contain nested cluster structure.
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© 1998 Springer Japan
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Gordon, A.D. (1998). How Many Clusters? An Investigation of Five Procedures for Detecting Nested Cluster Structure. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_9
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DOI: https://doi.org/10.1007/978-4-431-65950-1_9
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-70208-5
Online ISBN: 978-4-431-65950-1
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