Chapter 1: Transfer Learning in Non-Stationary Environments -- Chapter 2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift -- Chapter 3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams -- Chapter 4: Error-bounded Approximation of Data Stream: Methods and Theories -- Chapter 5: Ensemble Dynamics in Non-stationary Data Stream Classification -- Chapter 6: Processing Evolving Social Networks for Change Detection based on Centrality Measures -- Chapter 7: Large-scale Learning from Data Streams with Apache SAMOA -- Chapter 8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study -- Chapter 9: Detecting Smooth Cluster Changes in Evolving Graph Sequences -- Chapter 10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams -- Chapter 11: A Survey of Methods of Incremental Support Vector Machine Learning -- Chapter 12: On Social Network-based Algorithms for Data Stream Clustering.

This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.