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Abstract : |
Many real-life problems require a partial classi cation of the data. We use the term \partial classi cation" to describe the discovery of models that show characteristics of the data classes, but may not cover all classes and all examples of any given class. Complete classi cation may be infeasible or undesirable when there are a very large number of class attributes, most attributes values are missing, or the class distribution is highly skewed and the user is interested in understanding the low-frequency class. We show how association rules can be used for partial classi cation in such domains, and present two case studies: reducing telecommunications order failures and detecting redundant medical tests., |
