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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (13 chapters)
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10
A. Linear Algebra
12
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.

Inputs and outputs

k-means can handle mostly numeric features. Many tools provide categorical...

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