Studies in Big Data

Data Mining and Knowledge Discovery for Big Data

Methodologies, Challenge and Opportunities

Editors: Chu, Wesley W. (Ed.)

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  • Latest research on data mining
  • Presents foundations, social networks and applications
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About this book

The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation.

The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

Reviews

From the reviews:

“This book collects and collates the latest developments in data mining and knowledge discovery for big data … . This book is primarily for practicing professionals and researchers. It explains state-of-the-art methodologies, techniques, and developments in many fields of data mining. The compilation of the latest developments from diverse fields into one volume gives professionals an opportunity to learn what is happening in other fields and gain insights and knowledge that can be used in their own fields.” (Alexis Leon, Computing Reviews, February, 2014)

Table of contents (10 chapters)

Table of contents (10 chapters)
  • Aspect and Entity Extraction for Opinion Mining

    Zhang, Lei (et al.)

    Pages 1-40

  • Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data

    Li, Zhenhui (et al.)

    Pages 41-81

  • Spatio-temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities

    Faghmous, James H. (et al.)

    Pages 83-116

  • Mining Discriminative Subgraph Patterns from Structural Data

    Jin, Ning (et al.)

    Pages 117-152

  • Path Knowledge Discovery: Multilevel Text Mining as a Methodology for Phenomics

    Liu, Chen (et al.)

    Pages 153-192

Buy this book

eBook $119.00
price for USA in USD (gross)
  • ISBN 978-3-642-40837-3
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.99
price for USA in USD
  • ISBN 978-3-642-40836-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $159.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: October 14, 2016
  • ISBN 978-3-662-50945-6
  • Free shipping for individuals worldwide
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Data Mining and Knowledge Discovery for Big Data
Book Subtitle
Methodologies, Challenge and Opportunities
Editors
  • Wesley W. Chu
Series Title
Studies in Big Data
Series Volume
1
Copyright
2014
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-40837-3
DOI
10.1007/978-3-642-40837-3
Hardcover ISBN
978-3-642-40836-6
Softcover ISBN
978-3-662-50945-6
Series ISSN
2197-6503
Edition Number
1
Number of Pages
X, 311
Number of Illustrations
70 b/w illustrations, 29 illustrations in colour
Topics