Abstract
This chapter discusses clustering methods, which are commonly used to identify subgroups in the population of interest. The overall objective of these methods is to divide observations into several groups such that the observations in the same group are considered to be more similar compared to observations in two different groups. We define similarity using some appropriate distance measures. Then, we discuss two main classes of clustering methods, namely, K-means clustering and hierarchical clustering.
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References
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, Berlin (2009). http://www-stat.stanford.edu/~tibs/ElemStatLearn/
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Shahbaba, B. (2012). Clustering. In: Biostatistics with R. Use R!. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1302-8_12
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DOI: https://doi.org/10.1007/978-1-4614-1302-8_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-1301-1
Online ISBN: 978-1-4614-1302-8
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