Granular Computing Models and Methods Based on the Spatial Granulation

Abstract

Automatic data processing and extraction of rules from large datasets has gained considerable interest during the last years. Several approaches have been proposed, mainly based both statistical and fuzzy sets approaches. In this paper, we propose a new view to the approaches to represent the large datasets and to find rules in one. That makes use of so-called Information Granulation and Computing with Words methods. The basic Ideas and principles of Granular Computing have been studied explicitly or implicitly in many fields in isolation. With the recently renewed and fast growing interest, it is time to extract the commonality from a diversity of fields and to study systematically and formally the domain independent principles of Granular Computing in a unified model. A framework of granular computing can be improved by applying the inherited principles of Space Granulation and Computing with Shapes. In this paper, we examine a framework from new perspectives of granular computing, based on Space Granulation and Computing with Shapes for the granulated data processing and problem solving.