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Unsupervised Learning with R by Erik Rodríguez Pacheco

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Chapter 3. Identifying and Understanding Groups – Clustering Algorithms

This chapter aims to explain one of the most used techniques in unsupervised learning, the Clustering Analysis. Identifying groups can uncover and help to explain some patterns hiding in data and it is frequently the answer for multiple problems in many industries or contexts. Finding clusters can help to uncover relationships in data, which can, in turn, be used to support future decisions.

It is considered an unsupervised learning technique since its objective is to find relationships between study variables, but not the relations that these variables may have in relation to a target variable.

Typically, the application of clustering techniques involves five phases: developing ...

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