Abstract
In data mining process, visualisations assist the process of exploring data before modeling and exemplify the discovered knowledge into a meaningful representation. Visualisation tools are particularly useful for detecting patterns found in only small areas of the overall data. In this paper, we described a technique for discovering and presenting frequent pattern migrations in temporal social network data. The migrations are identified using the concept of a Migration Matrix and presented using a visualisation tool. The technique has been built into the Pattern Migration Identification and Visualisation (PMIV) framework which is designed to operate using trend clusters which have been extracted from big network data using a Self Organising Map technique. The PMIV is also aimed to detect changes in the characteristics of trend clusters and the existence of communities of trend clusters.
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Notes
- 1.
An epoch is defined in terms of a start and an end time stamp.
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Nohuddin, P.N.E., Coenen, F., Christley, R., Sunayama, W. (2015). Visualisation of Trend Pattern Migrations in Social Networks. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2015. Lecture Notes in Computer Science(), vol 9429. Springer, Cham. https://doi.org/10.1007/978-3-319-25939-0_7
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DOI: https://doi.org/10.1007/978-3-319-25939-0_7
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