scholarly journals Detection of Jihadism in Social Networks Using Big Data Techniques Supported by Graphs and Fuzzy Clustering

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Cristina Sánchez-Rebollo ◽  
Cristina Puente ◽  
Rafael Palacios ◽  
Claudia Piriz ◽  
Juan P. Fuentes ◽  
...  
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Fuzzy Clustering ◽  
Extraction Techniques ◽  
Public Database ◽  
New Members ◽  
Terrorist Organizations ◽  
Clustering Techniques ◽  
Level Of Activity ◽  
Data Architecture

Social networks are being used by terrorist organizations to distribute messages with the intention of influencing people and recruiting new members. The research presented in this paper focuses on the analysis of Twitter messages to detect the leaders orchestrating terrorist networks and their followers. A big data architecture is proposed to analyze messages in real time in order to classify users according to different parameters like level of activity, the ability to influence other users, and the contents of their messages. Graphs have been used to analyze how the messages propagate through the network, and this involves a study of the followers based on retweets and general impact on other users. Then, fuzzy clustering techniques were used to classify users in profiles, with the advantage over other classifications techniques of providing a probability for each profile instead of a binary categorization. Algorithms were tested using public database from Kaggle and other Twitter extraction techniques. The resulting profiles detected automatically by the system were manually analyzed, and the parameters that describe each profile correspond to the type of information that any expert may expect. Future applications are not limited to detecting terrorist activism. Human resources departments can apply the power of profile identification to automatically classify candidates, security teams can detect undesirable clients in the financial or insurance sectors, and immigration officers can extract additional insights with these techniques.

Improved Fuzzy Clustering Techniques for Categorical Data

2009 ◽  
Author(s):  
Indrajit Saha ◽  
Ujjwal Maulik ◽  
Sio-Iong Ao ◽  
Alan Hoi-Shou Chan ◽  
Hideki Katagiri ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Categorical Data ◽  
Clustering Techniques

Fuzzy Clustering

2017 ◽  
pp. 40-61 ◽  
Author(s):  
Mashhour H. Baeshen ◽  
Malcolm J. Beynon ◽  
Kate L. Daunt
Keyword(s):  
Data Analysis ◽  
Service Quality ◽  
Mobile Phone ◽  
Fuzzy Clustering ◽  
Data Set ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Fuzzy Environment ◽  
External Variables ◽  
Fuzzy C Means Clustering

This chapter presents a study of the development of the clustering methodology to data analysis, with particular attention to the analysis from a crisp environment to a fuzzy environment. An applied problem concerning service quality (using SERVQUAL) of mobile phone users, and subsequent loyalty and satisfaction forms the data set to demonstrate the clustering issue. Following details on both the crisp k-means and fuzzy c-means clustering techniques, comparable results from their analysis are shown, on a subset of data, to enable both graphical and statistical elucidation. Fuzzy c-means is then employed on the full SERVQUAL dimensions, and the established results interpreted before tested on external variables, namely the level of loyalty and satisfaction across the different clusters established.

Effective kernel-based possibilistic fuzzy clustering techniques: analyzing cancer database

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
S. R. Kannan ◽  
M. Siva ◽  
R. Devi ◽  
S. Ramathilagam ◽  
Mark Last
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Techniques

About the use of fuzzy clustering techniques for fuzzy model identification

1999 ◽  
Vol 106 (2) ◽  
pp. 179-188 ◽  
Author(s):  
A.F. Gómez-Skarmeta ◽  
M. Delgado ◽  
M.A. Vila
Keyword(s):  
Fuzzy Clustering ◽  
Model Identification ◽  
Fuzzy Model ◽  
Clustering Techniques

scholarly journals Sit-to-stand detection using fuzzy clustering techniques

2010 ◽  
Author(s):  
Tanvi Banerjee ◽  
James M. Keller ◽  
Marjorie Skubic ◽  
Carmen Abbott
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Techniques ◽  
Sit To Stand

Robust fuzzy clustering techniques for analyzing complicated colon cancer database

2014 ◽  
Vol 27 (5) ◽  
pp. 2573-2595
Author(s):  
S. Ramathilagam ◽  
R. Devi ◽  
Tzung-Pei Hong ◽  
S.R. Kannan
Keyword(s):  
Colon Cancer ◽  
Fuzzy Clustering ◽  
Clustering Techniques
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