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Practical Machine Learning with R

You're reading from   Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781838550134
Length 416 pages
Edition 1st Edition
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Authors (3):
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Brindha Priyadarshini Jeyaraman Brindha Priyadarshini Jeyaraman
Author Profile Icon Brindha Priyadarshini Jeyaraman
Brindha Priyadarshini Jeyaraman
Ludvig Renbo Olsen Ludvig Renbo Olsen
Author Profile Icon Ludvig Renbo Olsen
Ludvig Renbo Olsen
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
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Toc

Table of Contents (8) Chapters Close

About the Book 1. An Introduction to Machine Learning FREE CHAPTER 2. Data Cleaning and Pre-processing 3. Feature Engineering 4. Introduction to neuralnet and Evaluation Methods 5. Linear and Logistic Regression Models 6. Unsupervised Learning 1. Appendix

DIANA

DIANA stands for DIvisive ANAlysis clustering. It is a hierarchical clustering technique that starts with one cluster and subsequently divides the clusters until each cluster is just a single element. At each step, we select the cluster with the most dissimilar elements. The most dissimilar element within that cluster is then used as a starting point in the creation of a new cluster, consequently splitting the original cluster into two. Divisive coefficient is a unit that measures the amount of clustering structures found.

Note

For more information on DIANA, please refer to the work of Kaufman and Rousseeuw (1990), Finding groups in data. Rousseeuw, Peter J., and L. Kaufman. "Finding groups in data." Hoboken: Wiley Online Library (1990).

In the next exercise, we will be covering the concepts of hierarchical clustering using DIANA.

Exercise 61: Implement Hierarchical Clustering Using DIANA

In this exercise, we will perform hierarchal clustering using DIANA. We will be using the...

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