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Data Mining In Time Series And Streaming Databases [Kõva köide]

Edited by (-), Edited by (Univ Of South Florida, Usa), Edited by (Ben-gurion Univ Of The Negev, Israel)
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This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining.The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic.
Preface vii
About the Editors xi
About the Contributors xiii
Chapter 1 Streaming Data Mining with Massive Online Analytics (MOA)
1(25)
Albert Bifet
Jesse Read
Geoff Holmes
Bernhard Pfahringer
Chapter 2 Weightless Neural Modeling for Mining Data Streams
26(18)
Douglas O. Cardoso
Joao Gama
Felipe Franca
Chapter 3 Ensemble Classifiers for Imbalanced and Evolving Data Streams
44(25)
Dariusz Brzezinski
Jerzy Stefanowski
Chapter 4 Consensus Learning for Sequence Data
69(23)
Andreas Nienkotter
Xiaoyi Jiang
Chapter 5 Clustering-Based Classification of Document Streams with Active Learning
92(26)
Mark Last
Maxim Stoliar
Menahem Friedman
Chapter 6 Supporting the Mining of Big Data by Means of Domain Knowledge During the Pre-mining Phases
118(26)
Remon Cornelisse
Sunil Choenni
Chapter 7 Data Analytics: Industrial Perspective & Solutions for Streaming Data
144(25)
Mohsin Munir
Sebastian Baumbach
Ying Gu
Andreas Dengel
Sheraz Ahmed
Index 169