July 2017
Intermediate to advanced
360 pages
8h 26m
English
Content preview from Machine Learning Algorithms
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Summary
In this chapter, we have presented hierarchical clustering, focusing our attention on the agglomerative version, which is the only one supported by scikit-learn. We discussed the philosophy, which is rather different to the one adopted by many other methods. In agglomerative clustering, the process begins by considering each sample as a single cluster and proceeds by merging the blocks until the number of desired clusters is reached. In order to perform this task, two elements are needed: a metric function (also called affinity) and a linkage criterion. The former is used to determine the distance between the elements, while the latter is a target function that is used to determine which clusters must be merged.
We also saw how to ...
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