Mining and Analyzing Social Networks 2010 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 200 pages, kõrgus x laius x paksus: 235x155x11 mm, kaal: 308 g, 39 Illustrations, color; 200 p. 39 illus. in color., 1 Paperback / softback
  • Sari: Studies in Computational Intelligence 288
  • Ilmumisaeg: 28-Jun-2012
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642263496
  • ISBN-13: 9783642263491
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  • Formaat: Paperback / softback, 200 pages, kõrgus x laius x paksus: 235x155x11 mm, kaal: 308 g, 39 Illustrations, color; 200 p. 39 illus. in color., 1 Paperback / softback
  • Sari: Studies in Computational Intelligence 288
  • Ilmumisaeg: 28-Jun-2012
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642263496
  • ISBN-13: 9783642263491
Teised raamatud teemal:
Mining social networks has now becoming a very popular research area not only for data mining and web mining but also social network analysis. Data mining is a technique that has the ability to process and analyze large amount of data and by this to discover valuable information from the data. In recent year, due to the growth of social communications and social networking websites, data mining becomes a very important and powerful technique to process and analyze such large amount of data. Thus, this book will focus upon Mining and Analyzing social network. Some chapters in this book are extended from the papers that presented in MSNDS2009 (the First International Workshop on Mining Social Networks for Decision Support) and SNMABA2009 ((The International Workshop on Social Networks Mining and Analysis for Business Applications)). In addition, we also sent invitations to researchers that are famous in this research area to contribute for this book. The chapters of this book are introduced as follows: In chapter 1-Graph Model for Pattern Recognition in Text, Qin Wu et al. present a novel approach that uses a weighted directed multigraph for text pattern recognition. In the proposed methodology, a weighted directed multigraph model has been set up by using the distances between the keywords as the weights of arcs as well a keyword-frequency distance based algorithm has also been introduced. Case studies are also included in this chapter to show the performance is better than traditional means.
Graph Model for Pattern Recognition in Text
1(20)
Qin Wu
Eddie Fuller
Cun-Quan Zhang
Retrieving Wiki Content Using an Ontology
21(14)
Carlos Miguel Tobar
Alessandro Santos Germer
Juan Manuel Adan-Coello
Ricardo Luis de Freitas
Ego-Centric Network Sampling in Viral Marketing Applications
35(18)
Huaiyu (Harry) Ma
Steven Gustafson
Abha Moitra
David Bracewell
Integrating SNA and DM Technology into HR Practice and Research: Layoff Prediction Model
53(14)
Hui-Ju Wu
I-Hsien Ting
Huo-Tsan Chang
Actor Identification in Implicit Relational Data Sources
67(24)
Michael Farrugia
Aaron Quigley
Perception of Online Social Networks
91(16)
Travis Green
Aaron Quigley
Ranking Learning Entities on the Web by Integrating Network-Based Features
107(18)
Yingzi Jin
Yutaka Matsuo
Mitsuru Ishizuka
Discovering Proximal Social Intelligence for Quality Decision Support
125(14)
Yuan-Chu Hwang
Discovering User Interests by Document Classification
139(22)
Loc Nguyen
Network Analysis of Opto-Electronics Industry Cluster: A Case of Taiwan
161(22)
Ting-Lin Lee
Author Index 183

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