Network Analysis for Core Herbal Combination Knowledge Discovery from Clinical Chinese Medical Formulae

2009 ◽  
Author(s):  
Xuezhong Zhou ◽  
Runsun Zhang ◽  
Yinghui Wang ◽  
Ping Li ◽  
Baoyan Liu
Keyword(s):  
Network Analysis ◽  
Knowledge Discovery

Dynamic Social Network Mining

2013 ◽  
pp. 122-144
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Discovery ◽  
Network Mining ◽  
Social Network Mining ◽  
Dynamic Social Network ◽  
Main Work ◽  
Insight Into

This Chapter overviews most recent data mining approaches proposed in the context of social network analysis. In particular, it aims at classifying the proposed approaches based on both the adopted mining strategies and their suitability for supporting knowledge discovery in a dynamic context. To provide a thorough insight into the proposed approaches, main work issues and prospects in dynamic social network analysis are also outlined.

Applications of Data Mining in Dynamic Social Network Analysis

2013 ◽  
pp. 110-121
Author(s):  
Manish Kumar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Knowledge Discovery ◽  
Online Social Networking ◽  
Dynamic Social Network ◽  
Business Goals

Social Networks are nodes consisting of people, groups and organizations growing dynamically. The growth is horizontal as well as vertical in terms of size and number. Social network analysis has gained success due to online social networking and sharing sites. The accessibility of online social sites such as MySpace, Facebook, Twitter, Hi5, Friendster, SkyRock and Beb offer sharing and maintaining large amount of different data. Social network analysis is focused on mining such data i.e. generating pattern of people’s interaction. The analysis involves the knowledge discovery that helps the sites as well as users in terms of usage and business goals respectively. Further it is desired that the process must be privacy preserving. This chapter describes the various mining techniques applicable on social networks data.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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