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The main objective of this paper is to present granular computing algorithms of finding association rules with different levels of granularity from relational databases or information tables. Firstly, based on the partition model of granular computing, a framework for knowledge discovery in relational databases was introduced. Secondly, referring to granular computing, the algorithms for generating frequent k -itemsets were proposed. Finally, the proposed algorithms were illustrated with a real example and tested on two data sets under different supports. Experiment results show that the algorithms are effective and feasible. Moreover, the meanings of mining association rules based on granular computing are clearly understandable.

Ontology capture is a key topic in constructing an ontology, and it is a procedure of knowledge acquisition. In this paper, by applying the principle of granular computing, some concepts about information granules, including domain feature information granule, domain object information granule, were defined, the context of the domain ontology may be granulated. A domain granule and a domain granule lattice were presented. An algorithm of generating the domain granule lattice was proposed, and was illustrated by a real example. With the domain granule lattice, ontology capture can be fulfilled. Therefore, granular computing should be of great help in representing and constructing an ontology.