Interactive self-adaptive clutter-aware visualisation for mobile data mining

M. Gaber, S. Krishnaswamy, B. Gillick, H. AlTaiar, N. Nicoloudis, J. Liono, A. Zaslavsky

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There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualisation for emergency and disaster management, real-time optimisation for courier pick-up and delivery etc. There are many challenges in visualisation of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and e�ffective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualisation on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualisation technique that we term Clutter-Aware Clustering Visualiser CACV and its enhancement of enabling user interactivity that we term iCACV. Experimental results on both synthetic and real datasets using the Google Andriod platform are presented proving the e�ffectiveness of the proposed techniques.
Original languageEnglish
Pages (from-to)369-382
Number of pages14
JournalJournal of Computer and System Sciences
Issue number3
Publication statusPublished - May 2013


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