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A Multicriteria Approach to Data Summarization Using Concept Ontologies

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2 Author(s)
Yager, R.R. ; Machine Intelligence Inst., Iona Coll., New Rochelle, NY ; Petry, F.E.

This paper describes a conceptual and theoretical framework to allow better user control over data summarization for knowledge discovery. Basic to the approach is a measure of quality of summarization of data using categories provided by the hierarchical structure of concept ontology. This involves the modeling, using a fuzzy sets approach, of the four criteria implicit in a summarization imperative: minimum coverage, minimum relevance, succinctness, and usefulness. With these criteria modeled, a multicriteria approach is presented, using a decision function aggregating these criteria that provides an overall quality measure to guide the summarization of the data. The development of the theory is first presented for the simple case of a single attribute to clearly delineate the basic issues and approach and then extended to multiple attributes. Finally, approaches to provide a more user-oriented presentation of the summarized data are considered

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:14 ,  Issue: 6 )