MobiMine: monitoring the stock market from a PDA
ACM SIGKDD Explorations Newsletter
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Hand-OLAP: A System for Delivering OLAP Services on Handheld Devices
ISADS '03 Proceedings of the The Sixth International Symposium on Autonomous Decentralized Systems (ISADS'03)
ACM SIGMOD Record
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
A Taxonomy of Clutter Reduction for Information Visualisation
IEEE Transactions on Visualization and Computer Graphics
A holistic approach for resource-aware adaptive data stream mining
New Generation Computing
Visualising the cluster structure of data streams
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Interactive visualization of streaming data with Kernel Density Estimation
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Real-time classification of ECGs on a PDA
IEEE Transactions on Information Technology in Biomedicine
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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 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.