Temporal Association Rule Mining in Event Sequences

Temporal Association Rule Mining in Event Sequences

Sherri K. Harms (University of Nebraska at Kearney, USA)
Copyright: © 2005 |Pages: 5
DOI: 10.4018/978-1-59140-557-3.ch206
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Abstract

The emergence of remote sensing, scientific simulation and other survey technologies has dramatically enhanced our capabilities to collect temporal data. However, the explosive growth in data makes the management, analysis, and use of data both difficult and expensive. To meet these challenges, there is an increased use of data mining techniques to index, cluster, classify and mine association rules from time series data (Roddick & Spiliopoulou, 2002; Han, 2001). A major focus of these algorithms is to characterize and predict complex, irregular, or suspicious activity (Han, 2001).

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