Mining class-dependent rules using the concept of generalization/specialization hierarchies

Juliano Brito da Justa NevesMarina Teresa Pires Vieira

Data mining is the process of discovering useful and previously unknown patterns in large datasets. Discovering association rules between items in large datasets is one such data mining task. A discovered rule is considered interesting if it brings new and useful information about the data. In this paper we show that if a dataset can be divided into subclasses and analyzed as if it was a generalization/specialization hierarchy, the class/subclass relationship can lead to the discovery of class-dependent rules showing interesting differences between the behavior of the whole dataset and the behavior of each of its subclasses. These differences are extracted from the association rules mined from the whole dataset and from each subclass separately. Using the concept of generalization/specialization hierarchies, comparisons and tests of statistical hypotheses, we present methods that are able to efficiently discover class-dependent rules and which classes have a higher influence on each rule. They also show when a given stimulus can provoke on one class a response that was not expected if only the undivided dataset was analyzed.

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