Learning from Data Streams in Evolving Environments Methods and Applications /

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 interpr...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Sayed-Mouchaweh, Moamar (Editor)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Studies in Big Data, 41
Subjects:
Telecommunication.
Security systems.
Data mining.
Control engineering.
Communications Engineering, Networks.
Security Science and Technology.
Data Mining and Knowledge Discovery.
Control and Systems Theory.
Online Access:Full-text access
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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.

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