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Extracting representative information is of great interest in data queries and web applications nowadays, where approximate match between attribute values/records is an important issue in the extraction process. This paper proposes an approach to extracting representative tuples from data classes under an extended possibility-based data model, and to introducing a measure (namely, relation compactness) based upon information entropy to reflect the degree that a relation is compact in light of information redundancy. Theoretical analysis and data experiments show that the approach has desirable properties that: 1) the set of representative tuples has high degrees of compactness (less redundancy) and coverage (rich content); 2) it provides a way to obtain data query outcomes of different sizes in a flexible manner according to user preference; and 3) the approach is also meaningful and applicable to web search applications.
Neural Networks and Learning Systems, IEEE Transactions on (Volume:23 , Issue: 6 )
Date of Publication: June 2012