Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday 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 (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Incremental unsupervised learning using clustering


The concept behind clustering in a data stream remains the same as in batch or offline modes; that is, finding interesting clusters or patterns which group together in the data while keeping the limits on finite memory and time required to process as constraints. Doing single-pass modifications to existing algorithms or keeping a small memory buffer to do mini-batch versions of existing algorithms, constitute the basic changes done in all the algorithms to make them suitable for stream or real-time unsupervised learning.

Modeling techniques

The clustering modeling techniques for online learning are divided into partition-based, hierarchical-based, density-based, and grid-based, similar to the case of batch-based clustering.

Partition based

The concept of partition-based algorithms is similar to batch-based clustering where k clusters are formed to optimize certain objective functions such as minimizing the inter-cluster distance, maximizing...