Nohuddin, Puteri
Predictive trend mining for social network analysis.
Doctor of Philosophy thesis, University of Liverpool.
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Abstract
This thesis describes research work within the theme of trend mining as applied to social network data. Trend mining is a type of temporal data mining that provides observation into how information changes over time. In the context of the work described in this thesis the focus is on how information contained in social networks changes with time. The work described proposes a number of data mining based techniques directed at mechanisms to not only detect change, but also support the analysis of change, with respect to social network data. To this end a trend mining framework is proposed to act as a vehicle for evaluating the ideas presented in this thesis. The framework is called the Predictive Trend Mining Framework (PTMF). It is designed to support "end-to-end" social network trend mining and analysis. The work described in this thesis is divided into two elements: Frequent Pattern Trend Analysis (FPTA) and Prediction Modeling (PM). For evaluation purposes three social network datasets have been considered: Great Britain Cattle Movement, Deeside Insurance and Malaysian Armed Forces Logistic Cargo. The evaluation indicates that a sound mechanism for identifying and analysing trends, and for using this trend knowledge for prediction purposes, has been established.
| Item Type: | Thesis (Doctor of Philosophy) |
|---|---|
| Additional Information: | Date: 2012-05 (completed) |
| Uncontrolled Keywords: | Trend Mining, Frequent Pattern Mining, Social networks, Clustering, Trend cluster analysis |
| Subjects: | ?? QA75 ?? |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 02 Sep 2013 14:39 |
| Last Modified: | 16 Dec 2022 04:36 |
| DOI: | 10.17638/00007153 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/7153 |
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