Data Mining and Knowledge Discovery for Big Data

1. Front Matter

   Pages 1-8

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2. Aspect and Entity Extraction for Opinion Mining

   • Lei Zhang, Bing Liu

   Pages 1-40

3. Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data

   • Zhenhui Li, Jiawei Han

   Pages 41-81

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

   • James H. Faghmous, Vipin Kumar

   Pages 83-116

5. Mining Discriminative Subgraph Patterns from Structural Data

   • Ning Jin, Wei Wang

   Pages 117-152

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

   • Chen Liu, Wesley W. Chu, Fred Sabb, D. Stott Parker, Robert Bilder

   Pages 153-192

7. InfoSearch: A Social Search Engine

   • Prantik Bhattacharyya, Shyhtsun Felix Wu

   Pages 193-223

8. Social Media in Disaster Relief

   • Peter M. Landwehr, Kathleen M. Carley

   Pages 225-257

9. A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation

   • Chris Yang, Bhavani Thuraisingham

   Pages 259-280

10. A Clustering Approach to Constrained Binary Matrix Factorization

    • Peng Jiang, Jiming Peng, Michael Heath, Rui Yang

    Pages 281-303

11. Erratum: Data Mining and Knowledge Discovery for Big Data

    • Wesley W. Chu

    Pages 305-308

12. Back Matter

    Pages 309-309

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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

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)

• Department of Computer Science, University of California, Los Angeles, USA

  Wesley W. Chu

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