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Data Mining In Time Series And Streaming Databases

By: (Edited by) Abraham Kandel , (Edited by) Horst Bunke , (Edited by) Mark Last

Extended Catalogue

Ksh 14,400.00

Format: Hardback or Cased Book

ISBN-10: 9813228032

ISBN-13: 9789813228030

Series: Series In Machine Perception And Artificial Intelligence

Publisher: World Scientific Publishing Co Pte Ltd

Imprint: World Scientific Publishing Co Pte Ltd

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Feb 28th, 2018

Print length: 196 Pages

Weight: 480 grams

Dimensions (height x width x thickness): 23.10 x 17.20 x 1.80 cms

Product Classification: Data mining

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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.

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.


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