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- W1909813335 abstract "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." @default.
- W1909813335 created "2016-06-24" @default.
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- W1909813335 date "1999-01-01" @default.
- W1909813335 modified "2023-09-24" @default.
- W1909813335 title "An Appropriate Abstraction for an Attribute-Oriented Induction" @default.
- W1909813335 cites W2038858953 @default.
- W1909813335 doi "https://doi.org/10.1007/3-540-46846-3_5" @default.
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