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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

Chapter 7. Classification Analysis

In the context of data analysis, the main idea of classification is the partition of a dataset into labeled subsets. If the dataset is a table in a database, then this partitioning could amount to no more than the addition of a new attribute (that is, a new table column) whose domain (that is, range of values) is a set of labels.

For example, we might have the table of 16 fruits shown in Table 7-1:

Classification Analysis

Figure 7-1. The meta-algorithm generates the algorithm

The last column, labeled Sweet, is a nominal attribute that can be used to classify fruit: either it's sweet or it isn't. Presumably, every fruit type that exists could be classified by this attribute. If you see an unknown fruit in the grocery store and wonder whether it is sweet, a classification algorithm could predict the answer, based upon the other attributes that you can observe {Size, Color, Surface}. We will see how to do that later in the chapter.

A classification algorithm is a...

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