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

Explaining clustering analysis

According to Han (2011), Clustering is the process of grouping a set of data objects into multiple groups or clusters so that objects within a cluster have high similarity, but are very dissimilar to objects in other clusters (p. 443).

Imagine a dinner party has just started. You see a medium-sized rectangular room with a number of people in it. The room is filled with people who are socializing. You notice that people have formed small groups. What draws them together? It could be their existing friendship. Perhaps it is a future networking opportunity. Some groups may form for less obvious reasons. One thing you can say is that it would be unlikely to see everyone in the center of the room talking together as a single group of people. Keep this mental image in your mind because it represents an underlying aspect of cluster analysis.

Clusters are collections of points from a multidimensional set of data such that they minimize the distance between each cluster...