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

Chapter 5. Data Mining with Cluster Analysis

Data mining is a term that is been around since the 1990s. What exactly is data mining? Data mining is the process of working with a large amount of data to gather insights and detect patterns. Analysts often use it when the data does not include a response variable, yet there is a belief that a relationship or information about the structure of the data lies within it. This chapter will cover the following three introductory topics of data mining:

  • Explaining cluster analysis

  • Partitioning using k-means clustering

  • Clustering using hierarchical techniques

As in the previous chapters, you learned through use cases. There are two different use cases provided that teach data mining with two different cluster analysis approaches. Before we begin working, it is worthwhile to understand cluster analysis in context.