Learning from Data Streams in Evolving Environments

Chapter 1 Introduction

Chapter 2 Transfer Learning in Non-stationary Environments

Chapter 3 A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift

Chapter 4 Analyzing and Clustering Pareto-Optimal Objects in Data Streams

Chapter 5 Error-Bounded Approximation of Data Stream: Methods and Theories

Chapter 6 Ensemble Dynamics in Non-stationary Data Stream Classification

Chapter 7 Processing Evolving Social Networks for Change Detection Based on Centrality Measures

Chapter 8 Large-Scale Learning from Data Streams with Apache SAMOA

Chapter 9 Process Mining for Analyzing Customer Relationship Management Systems: A Case Study

Chapter 11 Efficient Estimation of Dynamic Density Functions with Applications in Data Streams

Chapter 12 Incremental SVM Learning: Review

Chapter 13 On Social Network-Based Algorithms for Data Stream Clustering