Data mining
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Some Criterions for Selecting the Best Data Abstractions
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
An Appropriate Abstraction for Construction a Compact Decision Tree
DS '00 Proceedings of the Third International Conference on Discovery Science
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An attribute-oriented induction is a useful data mining method that generalizes databases under an appropriate abstraction hierarchy to extract meaningful knowledge. The hierarchy is well designed so as to exclude meaningless rules from a particular point of view. However, there may exist several ways of generalizing databases according to user's intention. It is therefore important to provide a multi-layered abstraction hierarchy under which several generalizations are possible and are well controlled. In fact, too-general or too-specific databases are inappropriate for mining algorithms to extract significant rules. From this viewpoint, this paper proposes a generalization method based on an information theoretical measure to select an appropriate abstraction hierarchy. Furthermore, we present a system, called ITA (Information Theoretical Abstraction), based on our method and an attribute-oriented induction. We perform some practical experiments in which ITA discovers meaningful rules from a census database US Census Bureau and discuss the validity of ITA based on the experimental results.