Book Image

R Data Analysis Cookbook - Second Edition

By : Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan
Book Image

R Data Analysis Cookbook - Second Edition

By: Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan

Overview of this book

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.
Table of Contents (14 chapters)

Performing cluster validation

Clustering is an unsupervised machine learning approach to partition a dataset into a set of groups or clusters, but sometimes it has a tendency to form clusters even though the data does not contain any clusters. Hence, it's essential to validate the quality of the clustering output. Broadly, clustering validation statistics can be categorized into four classes:

  • Relative clustering validation: It evaluates the clustering structure by varying different parameter values for the same algorithm (namely, varying the number of clusters k).
  • Internal clustering validation: It uses internal information such as (silhouette width, Dunn index) of the clustering process to evaluate the goodness of a clustering structure.
  • External cluster validation: It uses ground truth information from the user about how the data should be grouped. As we know the true...