Kamal Ali and Stefanos Manganaris and Ramakrishnan Srikant
Many real-life problems require a partial classification of the data. We use the term "partial classification" 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 classification 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 classification in such domains, and present two case studies: reducing telecommunications order failures and detecting redundant medical tests.