Python: Real World Machine Learning

Introducing k-means clustering

In the previous section, you learned that unsupervised machine learning algorithms are used to extract key structural or information content from large, possibly complex datasets. These algorithms do so with little or no manual input and function without the need for training data (sets of labeled explanatory and response variables needed to train an algorithm in order to recognize the desired classification boundaries). This means that unsupervised algorithms are effective tools to generate information about the structure and content of new or unfamiliar datasets. They allow the analyst to build a strong understanding in a fraction of the time.

Clustering – a primer

Clustering is probably the archetypal unsupervised ...