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

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.

► A generic theory for visualisation termed Adaptive Clutter Reduction (ACR) has been proposed.
► Based on ACR, a novel visualisation method for data stream clustering has been introduced.
► Adaptability of the method to the screen clutter has been proved experimentally.