JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 1    Issue : 3    Date : June 2006

Mining Developing Trends of Dynamic Spatiotemporal Data Streams
Yu Meng and Margaret H. Dunham
Page(s): 43-50
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Abstract
This paper presents an efficient modeling technique for data streams in a dynamic spatiotemporal
environment and its suitability for mining developing trends. The streaming data are modeled using
a data structure that interleaves a semi-unsupervised clustering algorithm with a dynamic Markov
chain. The granularity of the clusters is calibrated using global constraints inherent to the data
streams. Novel operations are proposed for identifying developing trends. These operations include
deleting obsolete events using a sliding window scheme and identifying emerging events based on
a scoring scheme derived from the synopsis obtained from the modeling process. The proposed
technique is incremental, scalable, adaptive, and suitable for online processing. Algorithm analysis
and experiments demonstrate the efficiency and effectiveness of the proposed technique.

Index Terms
data mining, data stream, clustering, Markov chain, developing trend