Applied categorical data analysis
Applied categorical data analysis
C4.5: programs for machine learning
C4.5: programs for machine learning
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
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Classification is an important technique in data mining. The decision trees built by most of the existing classification algorithms commonly feature over-branching, which will lead to poor efficiency iu the subsequent classification period. In this paper, we present a new value-oriented classification method, which aims at building accurately proper-sized decision trees while reducing over-branching as much as possible, based on the concepts of frequent-pattern-node and exceptive-child-node. The experiments show that while using relevant analysis as pre-processing, our classification method, without loss of accuracy, can eliminate the over-branching greatly in decision trees more effectively and efficiently than other algorithms do.