Temporal Data Mining via Unsupervised Ensemble Learning

Nonfiction, Computers, Advanced Computing, Management Information Systems, Database Management, General Computing
Cover of the book Temporal Data Mining via Unsupervised Ensemble Learning by Yun Yang, Elsevier Science
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Author: Yun Yang ISBN: 9780128118412
Publisher: Elsevier Science Publication: November 15, 2016
Imprint: Elsevier Language: English
Author: Yun Yang
ISBN: 9780128118412
Publisher: Elsevier Science
Publication: November 15, 2016
Imprint: Elsevier
Language: English

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice.

Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem.

Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.

  • Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks
  • Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches
  • Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice.

Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem.

Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.

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