Anomaly detection in online social networks : using data-mining techniques and fuzzy logic


Autoria(s): Hassanzadeh, Reza
Data(s)

2014

Resumo

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/78679/

Publicador

Queensland University of Technology

Relação

http://eprints.qut.edu.au/78679/1/Reza_Hassanzadeh_Thesis.pdf

Hassanzadeh, Reza (2014) Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. PhD thesis, Queensland University of Technology.

Fonte

School of Electrical Engineering & Computer Science; Institute for Future Environments; Science & Engineering Faculty

Palavras-Chave #Anomaly Detection #Fuzzy Logig #Data Mining #Data Graph #Graph Theory
Tipo

Thesis