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An Associative Classification Method for detecting useful knowledge from huge multi-attributes dataset
An Associative Classification Method for detecting useful knowledge from huge multi-attributes dataset
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机译:一种从庞大的多属性数据集中检测有用知识的关联分类方法
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摘要
PURPOSE: An association classifying method for meaningful knowledge discovery in a high-capacity multi-attribute data set is provided to predict the class level of a test set more exactly. CONSTITUTION: Data is normalized(S110). Association rules are searched in consideration of the attribute of the normalized data(S120). A classification standard value is created(S130). The classification standard value compares all rules created by the classification degree of data and the association rule search. A rule standard value is created(S140). The rule standard value decides the range of rules in the creation of the rule using the median of a class. Rules are created on the basis of a target class label(S150).
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