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List of Figures
List of Tables
Preface

Ⅰ INTRODUCTION
1 Introduction
1.1 Big Data
1.2 Real-Time Analytics
1.3 What This Book Is About
2 Big Data Stream Mining
2.1 Algorithms
2.2 Classification
2.3 Regression
2.4 Clustering
2.5 Frequent Pattern Mining
3 Hands-on Introduction to MOA
3.1 Getting Started
3.2 The Graphical User Interface for Classification
3.3 Using the Command Line

Ⅱ STREAM MINING
4 Streams and Sketches
4.1 Setting: Approximation Algorithms
4.2 Concentration Inequalities
4.3 Sampling
4.4 Counting Total Items
4.5 Counting Distinct Elements
4.6 Frequency Problems
4.7 Exponential Histograms for Sliding Windows
4.8 Distributed Sketching: Mergeability
4.9 Some Technical Discussions and Additional Material
4.10 Exercises
5 Dealing with Change
5.1 Notion of Change in Streams
5.2 Estimators
5.3 Change Detection
5.4 Combination with Other Sketches and Multidimensional Data
5.5 Exercises
6 Classification
6.1 Classifier Evaluation
6.2 Baseline Classifiers
6.3 Decision Trees
6.4 Handing Numeric Attributes
6.5 Perceptron
6.6 Lazy Learning
6.7 Multi-label Classification
6.8 Active Learning
6.9 Concept Evolution
6.10 Lab Session with MOA
7 Ensemble Methods
7.1 Accuracy-Weighted Ensembles
7.2 Weighted Majority
7.3 Stacking
7.4 Bagging
7.5 Boosting
7.6 Ensembles of Hoeffding Trees
7.7 Recurrent Concepts
7.8 Lab Session with MOA
8 Regression
8.1 Introduction
8.2 Evaluation
8.3 Perceptron Learning
8.4 Lazy Learning
8.5 Decision Tree Learning
8.6 Decision Rules
8.7 Regression in MOA
9 Clustering
9.1 Evaluation Measures
9.2 The k-means Algorithm
9.3 BIRCH, BICO, and CLUSTREAM
9.4 Density-Based Methods: DBSCAN and Den-Stream
9.5 Clustree
9.6 StreamKM++: Coresets
9.7 Additional Material
9.8 Lab Session with MOA
10 Frequent Pattern Mining
10.1 An Introduction to Pattern Mining
10.2 Frequent Pattern Mining in Streams: Approaches
10.3 Frequent Itemset Mining on Streams
10.4 Freqent Subgraph Mining on Streams
10.5 Additional Material
10.6 Exercises

Ⅲ THE MOA SOFTWARE
11 Introduction to MOA and Its Ecosystem
11.1 MOA Architecture
11.2 Installation
11.3 Recent Developments is MOA
11.4 Extensions MOA
11.5 ADAMS
11.6 MEKA
11.7 OpenML
11.8 StreamsDM
11.9 Streams
11.10 Apache SAMOA
12 The Graphical User Interface
12.1 Getting Started with GUI
12.2 Classification and Regression
12.3 Clustering
13 Using the Command Line
13.1 Learning Task for Classification and Regression
13.2 Evaluation Tasks for Classification and Regression
13.3 Learning and Evaluation Tasks for Classification and Regressions
13.4 Comparing Two Classifiers
14 Using the API
14.1 MOA Objects
14.2 Options
14.3 Prequential Evaluation Example
15 Developing New Methods in MOA
15.1 Main Classes in MOA
15.2 Creating a New Classifier
15.3 Compiling a Classifier
15.4 Good Programming Practices in MOA

Bibliography
Index