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Data Mining and Knowledge Discovery for Big Data

SBD: DATA MINING & KNOWLEDGE DISCOVERY FOR BIG DATA

Information Technology

Computing & Information Technology

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B2B & B2C

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.

POST SECONDARY FOUR YEAR

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Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Preface.
Contents.
1: Aspect and Entity Extraction for Opinion Mining.
2: Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data.
3: Spatio-temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities.
4: Mining Discriminative Subgraph Patterns from Structural Data.
5: Path Knowledge Discovery: Multilevel Text Mining as a Methodology for Phenomics.
6: InfoSearch: A Social Search Engine.
7: Social Media in Disaster Relief: Usage Patterns, Data Mining Tools, and Current Research Directions.
8: A Generalized Approach for Social Network Integration and Analysis with Privacy Preservation.
9: A Clustering Approach to Constrained Binary Matrix Factorization.
Erratum: Data Mining and Knowledge Discovery for Big Data.
Foreword.
Author Index.

Data Mining and Knowledge Discovery for Big Data.css-1p8rvzy{position:absolute;padding-left:8px;}

Data Mining and Knowledge Discovery for Big Data