Interactive self-adaptive clutter-aware visualisation for mobile data mining
Mohamed Medhat Gaber,Shonali Krishnaswamy,Brett James Gillick,Hasnain Altaiar,Nicholas Nicoloudis,Jonathan Liono,Arkady Zaslavsky +6 more
- 01 May 2013
- Vol. 79, Iss: 3, pp 369-382
TL;DR: A generic theory for visualisation on small screens that is based on Adaptive Clutter Reduction ACR is proposed and a novel data stream clustering result visualisation technique that is developed and experimentally validated called Clutter-Aware Clustering Visualiser CACV and its enhancement of enabling user interactivity that is named iCACV are presented.
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Abstract: 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 effective 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 Android platform are presented proving the effectiveness of the proposed techniques.
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