Practical Guide to Clustering Algorithms & Evaluation in R
Introduction
Clustering algorithms are a part of unsupervised machine learning algorithms. Why unsupervised ? Because, the target variable is not present. The model is trained based on given input variables which attempts to discover intrinsic groups (or clusters).
Since target variable is not present we can't label those groups. Then, how is it done? That's the interesting part we'll look at in this article!
Clustering algorithms are widely used across all industries such as retail, banking, manufacturing, healthcare etc. In business terms, companies use it to separate customers sharing similar characteristics than others, in order to make customised engagement campaign strategies.
For example: In healthcare, a hospital might cluster patients based on their tumor size so that, patients with different tumor sizes can be treated differently.
Table of Contents
- Types of Clustering Techniques
- Distance Calculation for Clustering
- K means Clustering | How does it work?
- How to select best value of k in k means?
- Hierarchical Clustering | How does it work?
- What are the evaluation methods used in cluster analysis?
- Clustering in R - Water Treatment Plans
Complete Article - Read Here
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