LifeFlow: visualizing an overview of event sequences
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
2D and 3D representations for feature recognition in time geographical diary data
Information Visualization
Behavioural pattern identification and prediction in intelligent environments
Applied Soft Computing
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The ability to identify and examine patterns of activities is a key tool for social and behavioural science. In the past this has been done by statistical or purely visual methods but automated sequential pattern analysis through sophisticated data mining and visualization tools for pattern location and evaluation can open up new possibilities for interactive exploration of the data. This paper describes the addition of a sequential pattern identification method to the visual activity-analysis tool, VISUAL-TimePAcTS, and its effectiveness in the process of pattern analysis in social science diary data. The results have shown that the method correctly identifies patterns and conveys them effectively to the social scientist in a manner that allows them quick and easy understanding of the significance of the patterns.