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

Journal of Computer and System Sciences

Volumn 79, Issue 3, 2013, Pages 369-382

  Gaber, Mohamed Medhat ^a   Krishnaswamy, Shonali ^b,d   Gillick, Brett ^b   Altaiar, Hasnain ^b   Nicoloudis, Nicholas ^b   Liono, Jonathan ^b   Zaslavsky, Arkady ^c  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

^b MONASH UNIVERSITY   (Australia)

^c CSIRO   (Australia)

^d INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Adaptive clutter reduction; Clustering; Data mining; Data streams; Mobile computing; Visualisation

Indexed keywords

CLUSTERING ALGORITHMS; CLUTTER (INFORMATION THEORY); COMPUTATION THEORY; DATA STREAMS; DISASTER PREVENTION; DISASTERS; MEDICAL COMPUTING; MOBILE COMPUTING; PATIENT MONITORING; RADAR CLUTTER; REDUCTION; VISUALIZATION;

CLUSTERING; CLUTTER REDUCTION; COMPUTATIONAL CAPABILITY; DATA STREAM CLUSTERING; DATA STREAM PROCESSING; DISASTER MANAGEMENT; MOBILE HANDHELD DEVICES; REAL-TIME DATA STREAMS;

DATA MINING;

EID: 84872280586     PISSN: 00220000     EISSN: 10902724     Source Type: Journal    
DOI: 10.1016/j.jcss.2012.09.009     Document Type: Conference Paper

References (27)

1. Alfredo Cuzzocrea, Domenico Saccà, and Paolo Serafino Semantics-aware advanced OLAP visualization of multidimensional data cubes Int. J. Data Warehous. Min. 3 4 2007 1 30
2. Alfredo Cuzzocrea, Domenico Saccà, Paolo Serafino, A hierarchy-driven compression technique for advanced OLAP visualization of multidimensional data cubes, in: DaWaK, 2006, pp. 106-119.
3. Alfredo Cuzzocrea, Filippo Furfaro, Domenico Saccà: Hand-OLAP: A system for delivering OLAP services on handheld devices, in: ISADS 2003, pp. 80-87.
4. P. Domingos, G. Hulten, A general method for scaling up machine learning algorithms and its applications to clustering, in: Proceedings of the 18th Int. Conf. on Machine Learning, 2001.
5. M.M. Gaber, S. Krishnaswamy, and A. Zaslavsky On-board mining of data streams in sensor networks S. Badhyopadhyay, U. Maulik, L. Holder, D. Cook, Advanced Methods of Knowledge Discovery from Complex Data, A Chapter 2005 Springer
6. B. Gillick, S. Krishnaswamy, M.M. Gaber, A. Zaslavsky, Visualisation of fuzzy classification of data elements in ubiquitous data stream mining, in: IWUC 2006, 2006, pp. 29-38,.
7. M. Galan, H. Liu, and K. Torkkola Intelligent instance selection of data streams for smart sensor applications Intelligent Computing: Theory and Applications III Orlando, March 28-29 SPIE Proc. vol. 5803 2005 108 119
8. M.M. Gaber, and P.S. Yu Detection and classification of changes in evolving data streams Int. J. Inf. Technol. Decis. Mak. 5 4 2006
9. M.M. Gaber, A. Zaslavsky, and S. Krishnaswamy Mining data streams: A review ACM SIGMOD Record 34 1 June 2005
10. H. Kargupta, R. Bhargava, K. Liu, M. Powers, P. Blair, S. Bushra, J. Dull, K. Sarkar, M. Klein, M. Vasa, D. Handy, VEDAS: A mobile and distributed data stream mining system for real-time vehicle monitoring, in: Proc. of the SIAM DM Conference, 2004.
11. H. Kargupta, B. Park, S. Pittie, L. Liu, D. Kushraj, and K. Sarkar MobiMine: Monitoring the stock market from a PDA SIGKDD Explor. 3 2 January 2002
12. S.M. McConell, D.B. Skillicorn, A distributed approach for prediction in sensor networks, in: Workshop in Data Mining in Sensor Networks in Conj. with SIAM SDM Conference, USA, 2005.
13. J. Rodriguez, A. Goni, and A. Illarramendi Real-time classification of ECGs on a PDA IEEE Trans. Inf. Technol. Biomed. 9 1 2005 23 34
14. P. Radivojac, U. Korad, K.M. Sivalingam, Z. Obradovic, Learning from class-imbalanced data in wireless sensor networks, in: IEEE Semiannual Vehicular Technology Conference, VTC-Fall 2003, Orlando, FL, USA, October 2003, 2003, pp. 3030-3034.
15. R. Shah, S. Krishnaswamy, M.M. Gaber, Resource-aware very fast K-means for ubiquitous data stream mining, in: Proceedings of 2nd Int. Wshop on KD in Data Streams, ECML/PKDD 2005, 2005.
16. G. Ellis, and Alan Dix A taxonomy of clutter reduction for information visualisation IEEE Trans. Vis. Comput. Graph. 1077-2626 13 6 2007 1216 1223
17. M.M. Gaber, and P.S. Yu A holistic approach for resource-aware adaptive data stream mining J. New Gener. Comput. 0288-3635 25 1 November 2006 95 115
18. R. Bhargava, H. Kargupta, M. Powers, Energy consumption in data analysis for on-board and distributed applications, in: Proceedings of the ICMLÊ03 Workshop on Machine Learning Technologies for Autonomous Space Applications, 2003.
19. J. Bertin, Graphics and Graphic Information-Processing, New York, 1981.
20. S. Burigat, and L. Chittaro Visualizing the Results of Interactive Queries for Geographic Data on Mobile Devices 2005 ACM
21. L. Chittaro Visualizing information on mobile devices Computer 39 3 2006 40 45
22. S. Gustafson, and P. Baudisch Wedge: Clutter-Free Visualisation of Off-Screen Locations 2008 ACM
23. R. Spence Information Visualisation 2001 Addison-Wesley Reading, MA
24. S. Burigat, et al., Visualizing locations of off-screen objects on mobile devices: a comparative evaluation of three approaches, in: Proceedings of the 8th Conference on Human-Computer Interaction with Mobile Devices and Services, ACM, vol. 159, 2006, p. 246.
25. Dimitris K. Tasoulis, Gordon Ross, and Niall M. Adams Visualising the cluster structure of data streams Michael R. Berthold, John Shawe-Taylor, Nada Lavra, Proceedings of the 7th International Conference on Intelligent Data Analysis (IDAÊ07) 2007 Springer-Verlag Berlin, Heidelberg 81 92
26. Mohamed H. Ali, Badrish Chandramouli, Jonathan Fay, Curtis Wong, Steven M. Drucker, and Balan Sethu Raman Online visualization of geospatial stream data using the WorldWide telescope Proc. VLDB Endow., PVLDB 4 12 2011 1379 1382
27. Ove Daae Lampe, Helwig Hauser, Interactive visualization of streaming data with kernel density estimation, in: Proceedings of the 2011 IEEE Pacific Visualization Symposium, PacificVis, 2011, pp. 171-178.