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Hands-On Unsupervised Learning with Python
book

Hands-On Unsupervised Learning with Python

by Giuseppe Bonaccorso
February 2019
Intermediate to advanced content levelIntermediate to advanced
386 pages
9h 54m
English
Packt Publishing
Content preview from Hands-On Unsupervised Learning with Python

Minimizing the inertia

One of the biggest drawbacks of K-means and similar algorithms is the explicit request for the number of clusters. Sometimes this piece of information is imposed by external constraints (for example, in the example of breast cancer, there are only two possible diagnoses), but in many cases (when an exploratory analysis is needed), the data scientist has to check different configurations and evaluate them. The simplest way to evaluate K-means performance and choose an appropriate number of clusters is based on the comparison of different final inertias.

Let's start with the following simpler example based on 12 very compact Gaussian blobs generated with the scikit-learn function make_blobs():

from sklearn.datasets import ...
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Publisher Resources

ISBN: 9781789348279Supplemental Content