Recent granular computing frameworks for mining relational data

Abstract

A lot of data currently being collected is stored in databases with a relational structure. The process of knowledge discovery from such data is a more challenging task compared with single table data. Granular computing, which has successfully been applied to mining data storable in single tables, is a promising direction for discovering knowledge from relational data. This paper summarizes some recent developments in the area of application of granular computing to mining relational data. Four granular computing frameworks for processing relational data are introduced and compared. The paper shows how each of the frameworks represents relational data, constructs information granules and build patterns based on the granules. A generic system that can employ any of the frameworks to discover knowledge from relational data is also outlined.