Thesis Topic Details
Topic ID: |
3328 | |
Title: |
Incremental Discovery in Interaction Networks | |
Supervisor: |
Mike Bain | |
Research Area: |
Machine Learning, Bioinformatics | |
| Associated Staff | ||
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Assessor: |
Mark Temple | |
| Topic Details | ||
Status: |
Active | |
Type: |
R & D | |
Programs: |
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Group Suitable: |
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Industrial: |
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Pre-requisites: |
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Description: |
Project overview: With the growth of online applications in data-intensive areas like social media, bioinformatics, etc. there is an increasing need to analyse and mine interaction networks to discover interesting and potentially useful patterns. This project will investigate rule-based and alternative approaches to pattern discovery in interaction network graphs. Approach: Analysis of the structure of a number of interaction graphs from selected data sets will be necessary. Some way will be needed to encode graph 'semantics' and basic features of the interaction structures in a probabilistic rule-based form or related approach. A literature survey of current algorithms and methods will be done at the start of the project. Evaluation will be in terms of how well the system meets the design requirements, as well as assessment of the outputs in domain terms. Skills: Programming in one of PHP, Perl, Javascript or Java, possibly database design and implementation, simple user interface design and implementation. Knowledge of rule-based approaches, both practise and theory, would be useful. |
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Comments: |
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| Past Student Reports | ||
| Leyi ZHANG in s2, 2012 Incremental Discovery in Interaction Networks |
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