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Item Details
Title: SOCIAL NETWORK DATA ANALYTICS
By: Charu C. Aggarwal (Editor)
Format: Hardback

List price: £149.99


We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10: 1441984615
ISBN 13: 9781441984616
Publisher: SPRINGER-VERLAG NEW YORK INC.
Pub. date: 17 March, 2011
Edition: 2011 ed.
Pages: 502
Description: Filling an important niche in the field, this book covers a range of aspects of social network data mining, including Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks.
Synopsis: Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.
Illustrations: XIV, 502 p.
Publication: US
Imprint: Springer-Verlag New York Inc.
Returns: Returnable
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DATA STREAMS (HB)
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FREQUENT PATTERN MINING (HB)
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HEALTHCARE DATA ANALYTICS
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LINEAR ALGEBRA AND OPTIMIZATION FOR MACHINE LEARNING
LINEAR ALGEBRA AND OPTIMIZATION FOR MACHINE LEARNING (HB)
MACHINE LEARNING FOR TEXT
MACHINE LEARNING FOR TEXT (HB)
MACHINE LEARNING FOR TEXT (HB)
MANAGING AND MINING GRAPH DATA (HB)
MANAGING AND MINING GRAPH DATA (PB)
MANAGING AND MINING SENSOR DATA (HB)
MANAGING AND MINING SENSOR DATA (PB)
MANAGING AND MINING UNCERTAIN DATA (HB)
MANAGING AND MINING UNCERTAIN DATA (PB)
MINING TEXT DATA (HB)
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NEURAL NETWORKS AND DEEP LEARNING
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NEURAL NETWORKS AND DEEP LEARNING (HB)
OUTLIER ANALYSIS (HB)
OUTLIER ANALYSIS (HB)
OUTLIER ANALYSIS (PB)
OUTLIER ANALYSIS (PB)
OUTLIER DETECTION FOR TEMPORAL DATA (PB)
OUTLIER ENSEMBLES (HB)
OUTLIER ENSEMBLES (PB)
PRIVACY-PRESERVING DATA MINING (HB)
PRIVACY-PRESERVING DATA MINING (PB)
PROBABILITY AND STATISTICS FOR MACHINE LEARNING (HB)
RECOMMENDER SYSTEMS (HB)
RECOMMENDER SYSTEMS : THE TEXTBOOK (PB)
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