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Advanced Analytics with R and Tableau

You're reading from  Advanced Analytics with R and Tableau

Product type Book
Published in Aug 2017
Publisher Packt
ISBN-13 9781786460110
Pages 178 pages
Edition 1st Edition
Languages
Authors (3):
Ruben Oliva Ramos Ruben Oliva Ramos
Profile icon Ruben Oliva Ramos
Jen Stirrup Jen Stirrup
Profile icon Jen Stirrup
Roberto Rösler Roberto Rösler
View More author details

Table of Contents (16) Chapters

Advanced Analytics with R and Tableau
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Advanced Analytics with R and Tableau The Power of R A Methodology for Advanced Analytics Using Tableau and R Prediction with R and Tableau Using Regression Classifying Data with Tableau Advanced Analytics Using Clustering Advanced Analytics with Unsupervised Learning Interpreting Your Results for Your Audience Index

Clustering without using k-means


Now, Tableau can only do k-Means clustering. On the other hand, R can offer a variety of other clustering methodologies, such as hierarchical clustering.

In this topic, we will look at how R can do other types of clustering, which completes the picture of clustering in Tableau.

Hierarchical modeling

Hierarchical modeling is aimed at finding hierarchies of clusters. This facility is available to us in R. To do this, let's use the Iris dataset with an R script, which will focus on hierarchical clustering. The script is as follows:

IrisSample <- sample(1:dim(iris)[1],40)
IrisSample$Species <- NULL

dim(IrisSample)
hc <- hclust(dist(IrisSample), method="ave")
hc

plot(hc, hang = -1, labels=iris$Species[IrisSample])

In the following figure we can represent a cluster dendogram, which means the hierarchies of clusters.

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