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Java Data Analysis

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

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787285651

Length 412 pages

Edition 1st Edition

Languages

Java

Tools

RapidMiner

Concepts

Big Data

Author (1):

John R. Hubbard

View More author details

Table of Contents (14) Chapters

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

A. Java Tools

Index

Summary

After discussing various distance measures and the problems with high-dimensional clustering, this chapter presented four fundamental clustering algorithms: hierarchical clustering, k-means clustering, k-medoids clustering, and the affinity propagation algorithm. These were implemented here in Java, including methods provided by the Weka and the Apache Commons Math libraries.

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Authors (1)

John R. Hubbard

John R. Hubbard

John R. Hubbard has been doing computer-based data analysis for over 40 years at colleges and universities in Pennsylvania and Virginia. He holds an MSc in computer science from Penn State University and a PhD in mathematics from the University of Michigan. He is currently a professor of mathematics and computer science, Emeritus, at the University of Richmond, where he has been teaching data structures, database systems, numerical analysis, and big data. Dr. Hubbard has published many books and research papers, including six other books on computing. Some of these books have been translated into German, French, Chinese, and five other languages. He is also an amateur timpanist.

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