Abstract
There is currently a growing new focus in data mining – Ubiquitous Data Mining (UDM). UDM is the process of mining data streams in a ubiquitous environment, on resource constrained devices [KPP02]. UDM is widely applied in facilitating real-time decision making in mobile and highly dynamic environments/applications, such as road safety and mobile stock portfolio monitoring. A significant challenge in these contexts is the interpretation and analysis of results produced through unsupervised techniques (which are invaluable since little is known about the streamed data). We propose a novel fuzzy approach that leverages the significant benefits of UDM clustering and supplements the interpretation and use of these results through using expert/background knowledge.
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References
Charikar, M., Chekuri, C., Feder, T., Motwani, R.: Incremental Clustering and Dynamic Information Retrieval. In: Proceedings of the 29th annual ACM Symposium on Theory of Computing (1997)
Chen, R., Sivakumar, K., Kargupta, H.: An Approach to Online Bayesian Learning from Multiple Data Streams. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, Springer, Heidelberg (2001) and Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, Springer, Heidelberg (2001)
Flach, P.A., Mladenic, D., Moyle, S., Raeymaekers, S., Rauch, J., Rawles, S., Ribeiro, R., Sclep, G., Struyf, J., Todorovski, L., Torgo, L., Blockeel, H., Wettschereck, D., Wu, S., Gartner, T., Grobelnik, M., Kavsek, B., Kejkula, M., Krzywania, D., Lavrac, N., Ljubic, P.: On the road to knowledge: mining 21 years of UK traffic accident reports. In: Data Mining and Decision Support: Aspects of Integration and Collaboration, January 2003, pp. 143–155. Kluwer Academic Publishers, Dordrecht (2003)
Gaber, M.M., Krishnaswamy, S., Zaslavsky, A.: Cost-Efficient Mining Techniques for Data Streams. In: Australasian Workshop on Data Mining and Web Intelligence (DMWI 2004), Dunedin, New Zealand (2004)
Guha, S., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams. In: Proc. FOCS, pp. 359–366 (2000)
Grossman, R.: Supporting the Data Mining Process with Next Generation Data Mining Systems. Enterprise Systems (August 1998)
Gupta, C., Grossman, R.L.: GenIc: A Single Pass Generalized Incremental Algorithm for Clustering. In: International Conference Data Mining, SIAM 2004 (2004)
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. In: Proceedings of SIAM International Conference on Data Mining (April 2004)
Kargupta, H., Park, B., Pittie, S., Liu, L., Kushraj, D., Sarkar, K.: MobiMine: Monitoring the Stock Market from a PDA. ACM SIGKDD Explorations 3(2), 37–46 (2002)
Krishnaswamy, S., Loke, S., Zaslavsky, A.: Towards Anytime Anywhere Data Mining E-Services. In: Simoff, S.J., Williams, G.J., Hegland, M. (eds.) Proceedings of the Australian Data Mining Workshop (ADM 2002) at the 15th Australian Joint Conference on Artificial Intelligence, Canberra, Australia, December 2002, pp. 47–56. Published by the University of Technology Sydney (2002)
Moskowitz, H., Burns, M., Fiorentino, D., Smiley, A., Zador, P.: Driver Characteristics and Impairment at Various BACs, August 2000. Southern California Research Institute (2000)
Mendel, J.M.: Fuzzy logic systems for engineering: A tutorial. Proceedings of the IEEE 83(3), 345–377 (1995)
Oliver, N., Pentland, A.P.: Graphical Models for Driver Behavior Recognition in a SmartCar. In: MIT IEEE intelligent vehicles symposium (2000)
Singh, S.: Identification of Driver and Vehicle Characteristics through Data Mining the Highway Crash, National Highway Traffic Safety Administration, USA (2001)
Singh, S.: A Sampling Strategy for Rear-End Pre-Crash Data Collection, National Highway Traffic Safety Administration, USA (2001)
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Horovitz, O., Gaber, M.M., Krishnaswamy, S. (2005). Making Sense of Ubiquitous Data Streams – A Fuzzy Logic Approach. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_127
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DOI: https://doi.org/10.1007/11552451_127
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28895-4
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