CE: The Classifier-Estimator Framework for Data Mining [chapter]

M. M. Dalkilic, E. L. Roberston, D. Gucht
1998 Data Mining and Reverse Engineering  
The aim of this research is to establish a coherent framework for data mining in the relational model. Observing that data mining depends on two partitions, the classi er and the estimator, this paper de nes the classi er=estimator CE framework. The classi er indicates the target of the data mining investigation. The classi er may be di cult to express from the relational instance or may involve an oracle" beyond the extant data. The estimator is typically simply expressible using the
more » ... instance. The degree to which the estimator re nes the classi er partition can be used to measure how well the data instance matches the concept being investigated. The CE framework is shown to generalize a variety of data mining and database concepts, including rough sets, functional dependency, m ultivalued dependency, and association rules. Furthermore, the CE framework suggests a wider range of data mining questions. The CE framework is shown to naturally express qualitative and quantitative measures of the quality of approximation. Additionally, the CE framework allows a question to be posed at a number of di erent conceptual scopes from local to global interests.
doi:10.1007/978-0-387-35300-5_5 fatcat:ywffd5rj2jfedmxnpw3moezyaq