Efficient mining of emerging events in a dynamic spatiotemporal environment

Authors:
Yu Meng;Margaret H. Dunham
Affiliations:
Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas;Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

This paper presents an efficient data mining technique for modeling multidimensional time variant data series and its suitability for mining emerging events in a spatiotemporal environment. The data is modeled using a data structure that interleaves a clustering method with a dynamic Markov chain. Novel operations are used for deleting obsolete states, and finding emerging events based on a scoring scheme. The model is incremental, scalable, adaptive, and suitable for online processing. Algorithm analysis and experiments demonstrate the efficiency and effectiveness of the proposed technique.