An error-based conceptual clustering method for providing approximate query answers
Communications of the ACM - Electronic supplement to the December issue
Systems Approach to Computer-Integrated Design and Manufacturing
Systems Approach to Computer-Integrated Design and Manufacturing
Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization
IEEE Transactions on Knowledge and Data Engineering
Similarity Searching in Medical Image Databases
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
A Query System in a Biological Database
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Some inequalities relating different measures of divergence between two probability distributions
IEEE Transactions on Information Theory
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Neighbor knowledge construction is the foundation for the development of cooperative query answering systems capable of searching for close match or approximate answers when exact match answers are not available. This paper presents a technique for developing neighbor hierarchies at the attribute level. The proposed technique is called the evolved Pattern-based Knowledge Induction (ePKI) technique and allows construction of neighbor hierarchies for nonunique attributes based upon confidences, popularities, and clustering correlations of inferential relationships among attribute values. The technique is applicable for both categorical and numerical (discrete and continuous) attribute values. Attribute value neighbor hierarchies generated by the ePKI technique allow a cooperative query answering system to search for approximate answers by relaxing each individual query condition separately. Consequently, users can search for approximate answers even when the exact match answers do not exist in the database (i.e., searching for existing similar parts as part of the implementation of the concepts of rapid prototyping). Several experiments were conducted to assess the performance of the ePKI in constructing attribute-level neighbor hierarchies. Results indicate that the ePKI technique produces accurate neighbor hierarchies when strong inferential relationships appear among data.