Machine Learning Algorithms - Second Edition

Summary

In this chapter, we introduced the fundamental clustering algorithms, starting with k-NN, which is an instance-based method that can be employed whenever it's helpful to retrieve the most similar samples given a query point. Then, we discussed the Gaussian mixture approach, focusing on its peculiarities and requirements, discussing how it's possible to use it whenever a soft-clustering is preferable than a hard method.

The natural evolution of Gaussian mixture with null covariances leads to the K-means algorithm, which is based on the idea of defining (randomly, or according to some criteria) k centroids that represent the clusters and optimize their position so that the sum of squared distances for every point in each cluster and ...