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Java Data Analysis

Java Data Analysis

By : John R. Hubbard
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Java Data Analysis

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 (14 chapters)
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13
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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