February 2019
Intermediate to advanced
386 pages
9h 54m
English
Content preview from Hands-On Unsupervised Learning with Python
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K-means
K-means is the simplest implementation of the principle of maximum separation and maximum internal cohesion. Let's suppose we have a dataset X ∈ ℜM×N (that is, M N-dimensional samples) that we want to split into K clusters and a set of K centroids corresponding to the means of the samples assigned to each cluster Kj:
The set M and the centroids have an additional index (as a superscript) indicating the iterative step. Starting from an initial guess M(0), K-means tries to minimize an objective function called inertia (that is, the total average intra-cluster distance between samples assigned to a cluster Kj and its centroid μj):
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