April 2017
Beginner to intermediate
420 pages
9h 58m
English
book
Mastering Machine Learning with R - Second Edition
by Cory Lesmeister, Doug Ortiz, Vikram Dhillon, Miroslav Kopecky
Content preview from Mastering Machine Learning with R - Second Edition
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Hierarchical clustering
The hierarchical clustering algorithm is based on a dissimilarity measure between observations. A common measure, and what we will use, is Euclidean distance. Other distance measures are also available.
Hierarchical clustering is an agglomerative or bottom-up technique. By this, we mean that all observations are their own cluster. From there, the algorithm proceeds iteratively by searching all the pairwise points and finding the two clusters that are the most similar. So, after the first iteration, there are n-1 clusters, and after the second iteration, there are n-2 clusters, and so forth.
As the iterations continue, it is important to understand that in addition to the distance measure, we need to specify the linkage ...
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