Machine Learning for Data Streams:with Practical Examples in MOA


Machine Learning for Data Streams:with Practical Examples in MOA (Adaptive Computation and Machine Learning series) Hardcover – 10 April 2018
by:Albert Bifet , Ricard Gavaldà , Geoff Holmes , Bernhard Pfahringer , Francis Bach
pages 页数:288 pages
Publisher Finelybook 出版社:MIT Press (10 April 2018)
Language 语言:English
ISBN-10 书号:0262037793
ISBN-13 书号:9780262037792

Book Description
Machine Learning for Data Streams:with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.
Today many information sources―including sensor networks, financial markets, social networks, and healthcare monitoring―are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.
The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.Contents
Contents
List of Figures
List of Tables
Preface
I INTRODUCTION
1 Introduction
2 Big Data Stream Mining
3 Hands-on Introduction to MOA
II STREAM MINING
4 Streams and Sketches
5 Dealing with Change
6 Classification
7 Ensemble Methods
8 Regression
9 Clustering
10 Frequent Pattern Mining
III THE MOA SOFTWARE
11 Introduction to MOA and Its Ecosystem
12 The Graphical User Interface
13 Using the Command Line
14 Using the API
15 Developing New Methods in MOA
Bibliography
Index
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