Abstract
This paper addresses practical issues in k-means cluster analysis or segmentation with mixed types of variables and missing values. A more general k-means clustering procedure is developed that is suitable for use with very large datasets, such as arise in data mining and survey analysis. An exact assignment test guarantees that the algorithm will converge, and the detection of outliers allows the densest regions of the sample space to be mapped by tessellations of tightly-specified spherical clusters. A summary tree is obtained for the resulting k-cluster partition.
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Wishart, D. (2003). k-Means Clustering with Outlier Detection, Mixed Variables and Missing Values. In: Schwaiger, M., Opitz, O. (eds) Exploratory Data Analysis in Empirical Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55721-7_23
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DOI: https://doi.org/10.1007/978-3-642-55721-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44183-0
Online ISBN: 978-3-642-55721-7
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