Association Rules Mining of Glacier Inventory Data Based on Rough Set and Apriori Algorithm
Aiming at the mining about the glacier inventory data, the support and confidence thresholds of artificial given will result to excavate mass of meaningless of association rules. Presents a method of combining the significance of attributes of rough set and the probability of attribute values. Computes the significance of attributes respectively by rough set method; computes the probability of attribute values respectively; combines the significance of attributes and the probability of attribute values to obtain the weight of frequent itemsets. Dynamic changes the minimum support and minimum confidence of the Apriori algorithm by computing weighted average of the weight of itemsets, that not only enhances the efficiency of model, but also applies the mining to the glacier inventory data, and excavates the association between the features and elevation of glacier.