Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

CLICK HERE TO VISIT THE UPDATED SURVIVAL GUIDE

Chapter: Cluster Analysis

20200902 Cluster analysis(or clustering) is widely used in data mining to identify groups of similar data. It is well supported in R (R Core Team, 2020) with many packages available for preparing for cluster analysis, identifying a good number of clusters, performing a clustering, and evaluating the clustering. A variety of cluster analysis algorithms are available, each generating a cluster index for each data item, as the representation of the clustering. The measure of performance often involves measuring the distances of points withion a cluster and between clusters.

We have briefly introduced the KMeans clustering algorithm in decision trees as an algorithm in Section 16.7.