Abstract
This work investigates properties of a framework for mining relational data. The framework is constructed based on granular computing theory and is equipped with a method for deriving information granules from relational data. Such granules are the basis for discovering knowledge of a different type. It is shown in the paper that thanks to the properties one can improve the performance of tasks such as relational objects representation, search space limitation, and relational patterns generation.
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