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

Unsupervised learning using outlier detection


The subject of finding outliers or anomalies in the data streams is one of the emerging fields in machine learning. This area has not been explored by researchers as much as classification and clustering-based problems have. However, there have been some very interesting ideas extending the concepts of clustering to find outliers from data streams. We will provide some of the research that has been proved to be very effective in stream outlier detection.

Partition-based clustering for outlier detection

The central idea here is to use an online partition-based clustering algorithm and based on either cluster size ranking or inter-cluster distance ranking, label the clusters as outliers.

Here we present one such algorithm proposed by Koupaie et al., using incremental k-Means.

Inputs and outputs

Only numeric features are used, as in most k-Means algorithms. The number of clusters k and the number of windows of outliers n, on which offline clustering...