Conceptual querying through ontologies
Fuzzy Sets and Systems
Data mining by attribute generalization with fuzzy hierarchies in fuzzy databases
Fuzzy Sets and Systems
Ontological summaries through hierarchical clustering
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
IEEE Transactions on Fuzzy Systems
Mining negative generalized knowledge from relational databases
Knowledge-Based Systems
Genetic fuzzy markup language for game of NoGo
Knowledge-Based Systems
Summarization by Domain Ontology Navigation
International Journal of Intelligent Systems
Data summarization ontology-based query processing
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
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