You're reading from Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785880513

Length 556 pages

Edition 1st Edition

Languages

Java

Concepts

Big Data

Authors (2):

Kamath

Krishna Choppella

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Table of Contents (13) Chapters

Preface

1. Machine Learning Review FREE CHAPTER

2. Practical Approach to Real-World Supervised Learning

3. Unsupervised Machine Learning Techniques

4. Semi-Supervised and Active Learning

5. Real-Time Stream Machine Learning

6. Probabilistic Graph Modeling

7. Deep Learning

8. Text Mining and Natural Language Processing

9. Big Data Machine Learning – The Final Frontier

A. Linear Algebra

B. Probability

Index

Clustering

Clustering algorithms can be categorized in different ways based on the techniques, the outputs, the process, and other considerations. In this topic, we will present some of the most widely used clustering algorithms.

Clustering algorithms

There is a rich set of clustering techniques in use today for a wide variety of applications. This section presents some of them, explaining how they work, what kind of data they can be used with, and what their advantages and drawbacks are. These include algorithms that are prototype-based, density-based, probabilistic partition-based, hierarchy-based, graph-theory-based, and those based on neural networks.

k-Means

k-means is a centroid- or prototype-based iterative algorithm that employs partitioning and relocation methods (References [10]). k-means finds clusters of spherical shape depending on the distance metric used, as in the case of Euclidean distance.