Recursive meta-clustering in a granular network

Abstract

Granular computing represents an object as an information granule. Traditionally the information is derived from the primary source of data by recording events such as transactions, phone calls, user sessions, security breaches, and car trips. Much of the early data mining techniques used information granules generated from primary data sources. Recent data mining techniques such as ensemble classifiers and stacked regression use secondary sources of data obtained from initial data mining activities. Typically, these techniques use preliminary applications of data mining techniques for initial knowledge discovery. The knowledge acquired from the preliminary data mining is then used for more refined analysis. Granular computing can enable us to develop a formal framework for incorporating information from both primary and secondary sources of data. This enhanced granular representation can help us develop integrated data mining techniques. This paper proposes a novel recursive meta-clustering algorithm to demonstrate the versatility of granular computing for developing integrated data mining techniques to exploit primary and secondary knowledge sources.