Hierarchical Clustering: A Hands-On Guide with R
By
Bradley Boehmke & Brandon Greenwell
Fresh out the oven, still warm. Top of the tray.
Summary
This article explains hierarchical clustering as an alternative to k-means clustering for identifying groups in data sets. It highlights that hierarchical clustering creates a hierarchy of clusters without requiring a pre-specified number of clusters, and its results can be visualized using dendrograms. The content is part of a hands-on machine learning with R tutorial series, covering algorithmic deep dives.
Key quotes
· 3 pulledHierarchical clustering is an alternative approach to k-means clustering for identifying groups in a data set.
In contrast to k-means, hierarchical clustering will create a hierarchy of clusters and therefore does not require us to pre-specify the number of clusters.
Hierarchical clustering has an added advantage over k-means clustering in that its results can be easily visualized using an attractive tree-based representation called a dendrogram.
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