Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By : Javier Fernández González
Book Image

Mastering Concurrency Programming with Java 9 - Second Edition

By: Javier Fernández González

Overview of this book

Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs. The book starts with a full description of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications. The book ends with a detailed description of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

The first example - the k-means clustering algorithm

The k-means clustering algorithm is a clustering algorithm that groups a set of items not previously classified into a predefined number of clusters, K. It's very popular within the data mining and machine learning world, and is used in these fields to organize and classify data in an unsupervised way.

Each item is normally defined by a vector of characteristics or attributes (we use vector as a math concept, not as a data structure). All the items have the same number of attributes. Each cluster is also defined by a vector with the same number of attributes that represent all the items classified into that cluster. This vector is named the centroid. For example, if the items are defined by numeric vectors, the clusters are defined by the mean of the items classified into that cluster.

Basically, the algorithm has four steps:

  • Initialization: In the first step, you have to create the initial vectors that represent the K clusters. Normally...