Extracting Representative Information to Enhance Flexible Data Queries
Jin Zhang, Guoqing Chen, Xiaohui Tang · IEEE Transactions on Neural Networks and Learning Systems · 2012
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.