Email RIS file
Bookmark
Report an Issue

Data Mining and Knowledge Discovery for Big Data [electronic resource] : Methodologies, Challenge and Opportunities / edited by Wesley W. Chu

Author
Chu, Wesley W.
Published
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014.
Physical Description
X, 311 pages 99 illustrations, 29 illustrations in color : online resource
Additional Creators
SpringerLink (Online service)
Access Online

Availability

I Want It
I Want It

Finding items...

Series
Contents
Aspect and Entity Extraction for Opinion Mining -- Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data -- Spatio-Temporal Data Mining for Climate Data: Advances, Challenges -- Mining Discriminative Subgraph Patterns from Structural Data -- Path Knowledge Discovery: Multilevel Text Mining as a Methodology for Phenomics -- InfoSearch: A Social Search Engine -- Social Media in Disaster Relief: Usage Patterns, Data Mining Tools, and Current Research Directions -- A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation -- A Clustering Approach to Constrained Binary Matrix Factorization.
Summary
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.
Subject(s)
ISBN
9783642408373
Digital File Characteristics
text file PDF
Note
AVAILABLE ONLINE TO AUTHORIZED PSU USERS.
Part Of
Springer eBooks
View MARC record | catkey: 11538024