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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

Distance functions

Even if generic definitions of clustering are normally based on the concept of similarity, it's quite easy to employ its inverse, which is represented by distance function (dissimilarity measure). The most common choice is the Euclidean distance, but before choosing it, it's necessary to consider its properties and their behaviors in high-dimensional spaces. Let's start by introducing the Minkowski distance as a generalization of the Euclidean one. If the sample is xi ∈ ℜN, it is defined as:

For p=1, we obtain the Manhattan (or city block) distance, while p=2 corresponds to the standard Euclidean distance. We want to understand ...

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Publisher Resources

ISBN: 9781789348279Supplemental Content