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.

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Table of Contents (20 chapters)
Mastering Java Machine Learning
Mastering Java Machine Learning
Credits
Credits
Foreword
Foreword
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Machine Learning Review
Machine Learning Review
Machine learning – history and definition
What is not machine learning?
Machine learning – concepts and terminology
Machine learning – types and subtypes
Datasets used in machine learning
Machine learning applications
Practical issues in machine learning
Machine learning – roles and process
Machine learning – tools and datasets
Summary
2
Practical Approach to Real-World Supervised Learning
Practical Approach to Real-World Supervised Learning
Formal description and notation
Data transformation and preprocessing
Feature relevance analysis and dimensionality reduction
Model building
Model assessment, evaluation, and comparisons
Case Study – Horse Colic Classification
Summary
References
3
Unsupervised Machine Learning Techniques
Unsupervised Machine Learning Techniques
Issues in common with supervised learning
Issues specific to unsupervised learning
Feature analysis and dimensionality reduction
Clustering
Outlier or anomaly detection
Real-world case study
Summary
References
4
Semi-Supervised and Active Learning
Semi-Supervised and Active Learning
Semi-supervised learning
Active learning
Case study in active learning
Summary
References
5
Real-Time Stream Machine Learning
Real-Time Stream Machine Learning
Assumptions and mathematical notations
Basic stream processing and computational techniques
Concept drift and drift detection
Incremental supervised learning
Incremental unsupervised learning using clustering
Unsupervised learning using outlier detection
Case study in stream learning
Summary
References
6
Probabilistic Graph Modeling
Probabilistic Graph Modeling
Probability revisited
Graph concepts
Bayesian networks
Markov networks and conditional random fields
Specialized networks
Tools and usage
Case study
Summary
References
7
Deep Learning
Deep Learning
Multi-layer feed-forward neural network
Limitations of neural networks
Deep learning
Case study
Summary
References
8
Text Mining and Natural Language Processing
Text Mining and Natural Language Processing
NLP, subfields, and tasks
Issues with mining unstructured data
Text processing components and transformations
Topics in text mining
Tools and usage
Summary
References
9
Big Data Machine Learning – The Final Frontier
Big Data Machine Learning – The Final Frontier
What are the characteristics of Big Data?
Big Data Machine Learning
Batch Big Data Machine Learning
Case study
Linear Algebra
Linear Algebra
Vector
Matrix
Probability
Probability
Axioms of probability
Bayes' theorem
Index
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...

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