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|Title:||Abstract-Driven Pattern Discovery In Databases|
|Keywords:||pattern discovery;data abstraction;classification;generalization|
|Publisher:||Stern School of Business, New York University|
|Abstract:||In this paper, we study the problem of discovering interesting patterns in large volumes of data. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. We define a pattern as a deductive rule expressed in user-defined terms that has a degree of certainty associated with it. We present methods of discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user.|
|Appears in Collections:||IOMS: Information Systems Working Papers|
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