Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
About the Authors
About the Reviewers
Customer Feedback

What is Clustering?

Here we represent the elbow curve and the best number of clusters on it, which are represented on the curve line:

Clustering is a way of analyzing data so that the items are grouped into similar groups, or clusters, according to their similarity. Clustering is the process of finding interesting patterns in data, and it is used to categorize and classify data into groups, as well as to distinguish groups of data from each other. Before we start to cluster the data, we don't know the cluster where each data point resides.

Clustering is an example of an unsupervised method. In unsupervised methods of machine learning, unsupervised methods are not focused on trying to predict an outcome. Instead, unsupervised methods are focused on discovering patterns in the data. Using unsupervised methods means that you can take a fresh look at the data for patterns that you may not have considered previously, such as neural networks or clustering, for example.

Clustering is a great tool...