Objective classification of empirical probability distributions and the issue of event detection
DOI:
https://doi.org/10.26636/jtit.2008.3.882Keywords:
objective classification, event detection, multicriteria analysisAbstract
The paper concentrates on the issue of classification of empirical probability distributions (histograms), which is useful both in management situations and in event detection or event mining. While existing approaches to event detection concentrate on the use of selected moments or other characteristics of empirical probability distributions, we postulate that full empirical distribution preserves more of needed information then selected moments of this distribution, thus multiple criteria classification of distributions can be most effective in event detection.
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[1] J. Granat, „Events driven approach for supporting real-time management of complex systems”, in Proc. First World Congr. Int. Feder. Syst. Res. IFSR, Kobe, Japan, 2005.
View in Google Scholar
[2] J. Granat, „Event mining based on observations of the system”, J. Telecommun. Inform. Technol., no. 3, pp. 87-90, 2005.
View in Google Scholar
[3] J. Granat and A. P. Wierzbicki, „Multicriteria analysis in telecommunications”, in Proc. 37th Ann. Hawaii Int. Conf. Syst. Sci. HICSS’04, Big Island, Hawaii, USA, 2004.
View in Google Scholar
[4] J. Granat, „Multicriteria analysis for behavioral segmentation”, J. Telecommun. Inform. Technol., no. 3, pp. 39-43, 2004.
View in Google Scholar
[5] D. Luckham, The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Boston: Addison-Wesley, 2002.
View in Google Scholar
[6] L. Perrochon, W. Mann, S. Kasriel, and D. C. Luckham, „Event mining with event processing networks”, in Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS. London: Springer, 1999, vol. 1574, pp. 474-478.
View in Google Scholar
[7] A. P. Wierzbicki, „On the completeness and constructiveness of parametric characterizations to vector optimization problems”, OR Spectrum, vol. 8, pp. 73-87, 1986.
View in Google Scholar
[8] A. P. Wierzbicki, M. Makowski, and J. Wessels, Model-Based Decision Support Methodology with Environmental Applications. Dordrecht: Kluwer, 2000.
View in Google Scholar
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