Abstract
Attribute-oriented generalization (AOG) is a knowledge discovery method that uses generalization to simplify the descriptions of patterns in database data. AOG repeatedly replaces specific values for an attribute with more general concepts according to domain expert defined concept hierarchies. The degree of generalization is controlled by 2 userdefined thresholds. As presented by other researchers, the AOG process does not consider how interesting the results will be to the user. Given a relation retrieved from a database, many different relations can be created by generalization, some of which will be more interesting to the user than others. The attribute selection strategy, the method of choosing the next attribute for generalization, determines which of the many possible relations will be generated and thus can be used to direct the user towards the most interesting relations. We evaluate the performance of ten previously proposed and new attribute selection strategies by applying them to a 10,000 tuple public domain database and an 8,000,000 tuple commercial database. The strategies are compared using criteria that consider their ability to efficiently produce interesting results. We use measures of interestingness that consider the structure of the hierarchies that are used to guide generalization. Based on the comparison of the experimental results, a strategy that considers the complexity of the concept hierarchies was found to provide efficient and effective guidance towards interesting results.
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Barber, B., Hamilton, H.J. (1997). A comparison of atttribute selection strategies for attribute-oriented generalization. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1997. Lecture Notes in Computer Science, vol 1325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63614-5_10
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DOI: https://doi.org/10.1007/3-540-63614-5_10
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