Book Image

Java Data Analysis

By : John R. Hubbard
Book Image

Java Data Analysis

By: John R. Hubbard

Overview of this book

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Table of Contents (20 chapters)
Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Utility matrices


Most recommender systems use input that quantify users' preferences for items. These preferences are typically arranged in a matrix that has one row for each user and one column for each item. Such a matrix is called a utility matrix. For example, Netflix asks its users to rate movies from one to five stars. So, each entry in that utility matrix would be an integer uij in the range 0 to 5, representing the number of stars that user i gave to movie j, with 0 representing no rating.

For example, Table 9.1 shows a utility matrix that represents users' ratings of beers on a scale of 1-5, with 5 representing the greatest approval. Blanks represent no rating by that user for that item. The beers are: BL = Bud Light, G = Guinness, H = Heineken, PU = Pilsner Urquell, SA = Stella Artois, SNPA = Sierra Nevada Pale Ale, and W = Warsteiner.

Note

Most of the entries are blank.

 

BL

G

H

PU

SA

SNPA

W

x1

 

5

 

4

  

2

x2

2

 

3

  

5

3

x3

1

 

4

 

3

  

x4

3

4

 

5

 

4

 

x5

  ...