Learning from Data Streams in Evolving Environments

Description

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. Front Matter ....Pages i-viii Introduction (Moamar Sayed-Mouchaweh)....Pages 1-12 Transfer Learning in Non-stationary Environments (Leandro L. Minku)....Pages 13-37 A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift (Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghédira)....Pages 39-61 Analyzing and Clustering Pareto-Optimal Objects in Data Streams (Markus Endres, Johannes Kastner, Lena Rudenko)....Pages 63-91 Error-Bounded Approximation of Data Stream: Methods and Theories (Qing Xie, Chaoyi Pang, Xiaofang Zhou, Xiangliang Zhang, Ke Deng)....Pages 93-122 Ensemble Dynamics in Non-stationary Data Stream Classification (Hossein Ghomeshi, Mohamed Medhat Gaber, Yevgeniya Kovalchuk)....Pages 123-153 Processing Evolving Social Networks for Change Detection Based on Centrality Measures (Fabíola S. F. Pereira, Shazia Tabassum, João Gama, Sandra de Amo, Gina M. B. Oliveira)....Pages 155-176 Large-Scale Learning from Data Streams with Apache SAMOA (Nicolas Kourtellis, Gianmarco De Francisci Morales, Albert Bifet)....Pages 177-207 Process Mining for Analyzing Customer Relationship Management Systems: A Case Study (Ahmed Fares, João Gama, Pedro Campos)....Pages 209-221 Detecting Smooth Cluster Changes in Evolving Graph Structures (Sohei Okui, Kaho Osamura, Akihiro Inokuchi)....Pages 223-246 Efficient Estimation of Dynamic Density Functions with Applications in Data Streams (Abdulhakim Qahtan, Suojin Wang, Xiangliang Zhang)....Pages 247-278 Incremental SVM Learning: Review (Isah Abdullahi Lawal)....Pages 279-296 On Social Network-Based Algorithms for Data Stream Clustering (Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck)....Pages 297-317