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Generalization Data Mining in Fuzzy Object-Oriented Databases

Generalization Data Mining in Fuzzy Object-Oriented Databases
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Author(s): Rafal Angryk (Tulane University, USA), Roy Ladner (Naval Research Laboratory, USA) and Frederick E. Petry (Tulane University and Naval Research Laboratory, USA)
Copyright: 2008
Pages: 20
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch126


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In this chapter, we consider the application of generalization-based data mining to fuzzy similarity-based object-oriented databases (OODBs). Attribute generalization algorithms have been most commonly applied to relational databases, and we extend these approaches. A key aspect of generalization data mining is the use of a concept hierarchy. The objects of the database are generalized by replacing specific attribute values by the next higher-level term in the hierarchy. This will then eventually result in generalizations that represent a summarization of the information in the database. We focus on the generalization of similarity-based simple fuzzy attributes for an OODB using approaches to the fuzzy concept hierarchy developed from the given similarity relation of the database. Then consideration is given to applying this approach to complex structure-valued data in the fuzzy OODB.

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