Temporal Data Mining via Unsupervised Ensemble Learning

Yun Yang (School of Software, Yunnan University, China)

$89.95

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Elsevier Science Publishing Co Inc
21 November 2016

Database programming; Data mining; Artificial intelligence

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.

By: Yun Yang (School of Software Yunnan University China)
Imprint: Elsevier Science Publishing Co Inc
Country of Publication: United States
Edition: UK ed.
Dimensions: Height: 234mm, Width: 191mm,
Weight: 450g
ISBN: 9780128116548
ISBN 10: 0128116544
Pages: 172
Publication Date: 21 November 2016
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

1. Introduction2. Temporal Data Mining3. Temporal Data Clustering4. Ensemble Learning5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations8. Conclusions, Future Work

Dr Yang has a very solid and broad knowledge and experience in computer science, and in-depth expertise in machine learning, data mining and temporal data processing. His main research area is in the temporal data mining and unsupervised ensemble learning. In these topics, he has produced some internationally excellent research results including proposing and developing several innovation methods and algorithms. These works have been published in the international leading research journals or conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics- Part C, and Knowledge-Based Systems. His research results have attracted a lot of attentions from the machine learning research community and made the significant impact. As an evidence to illustrate the attention that his work has received and the impact his work has produced, his IEEE Transaction publication “Temporal data clustering via weighted clustering ensemble with different representations has been cited more than 42 times based on Google scholar.