Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Chapter 5. Unsupervised Learning - Clustering and Dimensionality Reduction

In the previous chapter, covered with with machine learning in Java and discussed how to approach the supervised learning problem when the label information is provided.

Often, however, there is no label information, and all we have is just some data. In this case, it is still possible to use machine learning, and this class of problems is called unsupervised learning; there are no labels, hence no supervision. Cluster analysis belongs to one of these algorithms. Given some dataset, the goal is to group the items from there such that similar items are put into the same group.

Additionally, some unsupervised learning techniques can be useful when there is label information.

For example, the dimensionality reduction algorithm tries to compress the dataset such that most of the information is preserved and the dataset can be represented with fewer features. What is more, dimensionality reduction is also useful for performing...