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This document presents a novel enrolments forecasting model based on automatic clustering techniques and time-variant fuzzy logical relationship groups. The model combines these two computational methods to forecast student enrollment at the University of Alabama. Specifically, it uses an automatic clustering algorithm to divide historical enrollment data into intervals of unequal length. Then it calculates time-variant fuzzy logical relationships within these new intervals to generate enrollment forecasts.

Author

erpublication

Language

EN