Book Image

Introduction to R for Business Intelligence

By : Jay Gendron
Book Image

Introduction to R for Business Intelligence

By: Jay Gendron

Overview of this book

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
Table of Contents (19 chapters)
Introduction to R for Business Intelligence
About the Author
About the Reviewers
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations

Clustering using hierarchical techniques

Hierarchical clustering techniques approach the analysis a bit differently than k-means clustering. Instead of working with a predetermined number of centers and iterating to find membership, hierarchical techniques continually pair or split data into clusters based on similarity (distance). There are two different approaches:

  • Divisive clustering: This begins with all the data in a single cluster and then splits it and all subsequent clusters until each data point is its own individual cluster

  • Agglomerative clustering: This begins with each individual data point and pairs them together in a hierarchy until there is just one cluster

In this section, you will learn and use agglomerative hierarchical clustering. It is a bit faster than divisive clustering, but they both may work slow with very large datasets. One benefit of hierarchical approaches is that they do not require you to specify the number of clusters in advance. You can run the model and prune...