Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
482356 | European Journal of Operational Research | 2006 | 9 Pages |
Abstract
In data mining, the unsupervised learning technique of clustering is a useful method for ascertaining trends and patterns in data. Most general clustering techniques do not take into consideration the time-order of data. In this paper, mathematical programming and statistical techniques and methodologies are combined to develop a seasonal clustering technique for determining clusters of time series data. We apply this technique to weather and aviation data to determine probabilistic distributions of arrival capacity scenarios, which can be used for efficient traffic flow management. In general, this technique may be used for seasonal forecasting and planning.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Tasha R. Inniss,