© Arnaldo Pérez Castaño 2018
Arnaldo Pérez CastañoPractical Artificial Intelligencehttps://doi.org/10.1007/978-1-4842-3357-3_13

13. Clustering & Multi-objective Clustering

Arnaldo Pérez Castaño1 
(1)
Havana, Cuba
 

Thus far, we have discussed several methods related to supervised learning. In these methods, we approximated a function from a training data set containing labeled data. In this chapter, we will begin addressing unsupervised learning, a paradigm of machine learning where we deduce a function and the structure of data from an unlabeled data set.

Unsupervised learning (UL) methods no longer have a “training” data set. Consequently, the training phase in UL disappears because data does not have an associated classification; the correct classification ...

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