Visualisation of Fuzzy Classification of Data Elements in Ubiquitous Data Stream Mining

Brett Gillick, Shonali Krishnaswamy, Mohamed Medhat Gaber, Arkady Zaslavsky

2006

Abstract

Ubiquitous data mining (UDM) allows data mining operations to be performed on continuous data streams using resource limited devices. Visualisation is an essential tool to assist users in understanding and interpreting data mining results and to aide the user in directing further mining operations. However, there are currently no on-line real-time visualisation tools to complement the UDM algorithms. In this paper we investigate the use of visualisation techniques, within an on-line real-time visualisation framework, in order to enhance UDM result interpretation on handheld devices. We demonstrate a proof of concept implementation for visualising degree of membership of data elements to clusters produced using fuzzy logic algorithms.

References

  1. Aggarwal, C. C., Han, J., Wang, J., Yu, P. S.: A Framework for Clustering Evolving Data Streams, Proc. 2003 Int. Conf. on Very Large Data Bases (VLDB'03), Berlin, Germany (2003)
  2. Aggarwal, C. C.: A Framework for Diagnosing Changes in Evolving Data Streams. Proceedings of the ACM SIGMOD Conference (2003)
  3. Aggarwal, C. C.: On Change Diagnosis in Evolving Data Streams. IEEE Transactions on Knowledge and Data Engineering 17(5), (2005) 587-600
  4. Babcock, B., Datar, M., Motwani, R., O'Callaghan, L.: Maintaining Variance and kMedians over Data Stream Windows, Proceedings of the 2003 ACM Symposium on Principles of Database Systems (PODS 2003) (2003)
  5. Domingos, P., Hulten, G.: A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering, Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA (2001) 106-113
  6. Gaber, M. M., Krishnaswamy, S., Zaslavsky, A.: Cost-Efficient Mining Techniques for Data Streams, Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), Dunedin, New Zealand (2004)
  7. Gollapudi, S., Sivakumar, D.: Framework and algorithms for trend analysis in massive temporal data sets, presented at Thirteenth ACM conference on Information and knowledge management, Washington, D.C., USA (2004)
  8. Guha, S., Mishra, N., Motwani, R., O'Callaghan, L.: Clustering data streams, in Proc. FOCS, (2000) 359-366
  9. Healey, C. G., Booth, K. S., Enns, J.: Visualizing Real-Time Multivariate Data Using Preattentive Processing, ACM Transactions on Modeling and Computer Simulation 5, 3 (1995) 190-221.
  10. Kargupta, H., Park, B., Pittie, S., Liu, L., Kushraj, D., Sarkar, K.: MobiMine: Monitoring the Stock Market from a PDA. ACM SIGKDD Explorations, Volume 3, Issue 2. ACM Press (2002) 37-46
  11. Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., Dull, J., Sarkar, K., Klein, M., Vasa, M., Handy, D.: VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring. Accepted for publication in the Proceedings of the SIAM International Data Mining Conference, Orlando. (2004)
  12. Keim, D. A.: Information visualization and visual data mining. IEEE Transactions On Visualization And Computer Graphics, 8(1) (2002) 1-8
  13. Keim, D. A., Schneidewind, J., Sips, M.: CircleView: a new approach for visualizing timerelated multidimensional data sets. AVI 2004 (2004) 179-182
  14. Moskowitz, H., Burns, M., Fiorentino, D., Smiley, A., Zador, P.: Driver Characteristics and Impairment at Various BACs, Southern California Research Institute (2000)
  15. O'Callaghan, L., Mishra, N., Meyerson, A., Guha, S., Motwani, R.: Streaming-data algorithms for high-quality clustering. Proceedings of IEEE International Conference on Data Engineering (2002)
  16. Wegman, E., Marchette, D.: On some techniques for streaming data: A case study of Internet packet headers, Journal of Computational and Graphical Statistics, 12(4) (2003) 893- 914
  17. Wong, P. C., Foote, H., Adams, D., Cowley, W., Thomas, J.: Dynamic Visualization of Transient Data Streams, IEEE Symposium on Information Visualization (2003)
  18. Zaki, M. J.: Online, Interactive and Anytime Data Mining, guest editorial for special issue of SIGKDD Explorations, Volume 3, Issue 2 (2002) i-ii
Download


Paper Citation


in Harvard Style

Gillick B., Krishnaswamy S., Medhat Gaber M. and Zaslavsky A. (2006). Visualisation of Fuzzy Classification of Data Elements in Ubiquitous Data Stream Mining . In Proceedings of the 3rd International Workshop on Ubiquitous Computing - Volume 1: IWUC, (ICEIS 2006) ISBN 978-972-8865-51-1, pages 29-38. DOI: 10.5220/0002485700290038


in Bibtex Style

@conference{iwuc06,
author={Brett Gillick and Shonali Krishnaswamy and Mohamed Medhat Gaber and Arkady Zaslavsky},
title={Visualisation of Fuzzy Classification of Data Elements in Ubiquitous Data Stream Mining},
booktitle={Proceedings of the 3rd International Workshop on Ubiquitous Computing - Volume 1: IWUC, (ICEIS 2006)},
year={2006},
pages={29-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002485700290038},
isbn={978-972-8865-51-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Workshop on Ubiquitous Computing - Volume 1: IWUC, (ICEIS 2006)
TI - Visualisation of Fuzzy Classification of Data Elements in Ubiquitous Data Stream Mining
SN - 978-972-8865-51-1
AU - Gillick B.
AU - Krishnaswamy S.
AU - Medhat Gaber M.
AU - Zaslavsky A.
PY - 2006
SP - 29
EP - 38
DO - 10.5220/0002485700290038