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Approximate Optimal Decision Tree Generation Algorithm Based On Rough Set

Xin Ai

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Abstract

Data mining is an important data analysis method,and decision tree is one of main techniques in data mining.A problem concerned by many researches is how to construct a decision tree.In this paper,based on rough set,an attribute and attribute value reduc-tion can be used to delete redundant decision information in a decision table.On the basis of compact information an approximate optimal decision tree is built.An Algorithm to generate decision tree is proposed in this paper,and it is illustrated by using a real example.

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What this paper is about

Data mining is an important data analysis method,and decision tree is one of main techniques in data mining.A problem concerned by many researches is how to construct a decision tree.In this paper,based on rough set,an attribute and attribute value reduc-tion can be used to delete redundant decision information in a decision table.On the basis of compact information an approximate optimal decision tree is built.An Algorithm to generate decision tree is proposed in this paper,and it is illustrated by using a real example.

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Available abstract

Data mining is an important data analysis method,and decision tree is one of main techniques in data mining.A problem concerned by many researches is how to construct a decision tree.In this paper,based on rough set,an attribute and attribute value reduc-tion can be used to delete redundant decision information in a decision table.On the basis of compact information an approximate optimal decision tree is built.An Algorithm to generate decision tree is proposed in this paper,and it is illustrated by using a real example.

Key concepts: Incremental decision tree, Decision tree, ID3 algorithm, Rough set, Data mining, Decision table, Decision tree learning, Computer science

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