Generalized decision tree for managing classification rules

A generalized decision tree was developed to effectively store, prune and use large amounts of meaningful rules used by various classification algorithms which can not be managed by traditional decision trees. The system extends the structure of traditional decision trees so it can store all the classification rules found with less storage cost and can more easily express the relationships between rules. An effective optimization strategy was developed to speed up the rule search process. The structure can generalize and unify decision tree classifications and classifications based on association rules. Test results show that the generalized decision tree overcomes the weaknesses of traditional decision trees and can be easily maintained, pruned and searched.