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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (20) Chapters Close

Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Data Analysis FREE CHAPTER 2. Data Preprocessing 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java Java Tools Index

Large sparse matrices


In commercial implementations of these recommender systems, the utility and similarity matrices would be far too large to be stored as internal arrays. Amazon, for example, has millions of items for sale and hundreds of millions of customers. With m = 100,000,000 and n = 1,000,000, the utility matrix would have m·n = 100,000,000,000,000 slots and the similarity matrix would have n2 = 1,000,000,000,000 slots. Moreover, if the average customer buys 100 items, then only 100n = 100,000,000 of the entries of the utility matrix would be non-zero—that's only 0.0001 percent of the entries, making it a very sparse matrix.

A sparse matrix is a matrix in which nearly all the entries are zero. Even if possible, it is very inefficient to store such a matrix as a two-dimensional array. In practice, other data structures are used.

There are several data structures that are good candidates for storing sparse matrices. A map is a data structure that implements a mathematical function...

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