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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.
Table of Contents
Chapter1: Transfer Learning in Non-Stationary Environments.- Chapter2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift.- Chapter3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams.- Chapter4: Error-bounded Approximation of Data Stream: Methods and Theories.- Chapter5: Ensemble Dynamics in Non-stationary Data Stream Classification.- Chapter6: Processing Evolving Social Networks for Change Detection based on Centrality Measures.- Chapter7: Large-scale Learning from Data Streams with Apache SAMOA.- Chapter8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study.- Chapter9: Detecting Smooth Cluster Changes in Evolving Graph Sequences.- Chapter10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams.- Chapter11: A Survey of Methods of Incremental Support Vector Machine Learning.- Chapter12: On Social Network-based Algorithms for Data Stream Clustering.
