Data Mining and Knowledge Discovery for Big Data

Methodologies, Challenge and Opportunities

  • Wesley W. Chu

Part of the Studies in Big Data book series (SBD, volume 1)

Table of contents

  1. Front Matter
    Pages 1-8
  2. Lei Zhang, Bing Liu
    Pages 1-40
  3. Chen Liu, Wesley W. Chu, Fred Sabb, D. Stott Parker, Robert Bilder
    Pages 153-192
  4. Prantik Bhattacharyya, Shyhtsun Felix Wu
    Pages 193-223
  5. Peter M. Landwehr, Kathleen M. Carley
    Pages 225-257
  6. Peng Jiang, Jiming Peng, Michael Heath, Rui Yang
    Pages 281-303
  7. Back Matter
    Pages 309-309

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.


Computational Intelligence Davis Social Links Foundation on Data Mining and Learning MoveMining Opining Mining Path Mining for Phonemics Social Media Mining

Editors and affiliations

  • Wesley W. Chu
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaLos AngelesUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-40836-6
  • Online ISBN 978-3-642-40837-3
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site