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

Partitioning using k-means clustering

The goal of partitioning is to place partitions and create clusters that reduce the within cluster sum of square error. In an extreme case, you could achieve a zero sum of square error if every data point existed in its own cluster. This would not be very useful though, would it? So partitioning is about finding the balance between reducing error and finding the right number of clusters.

A commonly used partitioning method is k-means. You will more often see it referred to as k-means clustering. K-means clustering places centers at k locations in the observation space to serve as the means of these k clusters. For example, if you were performing k-means clustering with k = 3, you would place three cluster means somewhere in the data space to set the initial conditions of the analysis.

K-means iteratively steps through the following three primary steps:

  1. Specify the number of clusters, k. Assign their initial locations randomly or in specific locations.

  2. The...