Abstract
We proposed a method to extract knowledge about associations between
attributes of data by fuzzy sets and fuzzy qualifiers, where it is expressed in a form of natural language for a human to understand easily the meaning about the data. The knowledge associates local values in some attribute with those in another attribute. In this paper, we extend the method by extracting global associations between two attributes. For example, "The data of high degree of property that the greater value in the attribute A gets greater in B is classified into the class C1". We normalize values for each attribute so that the average is 0 and the standard deviation is 1. We express normalized data in polar coordinates to extract associations between attributes by restricting arguments with fuzzy sets. The method extracts knowledge about global associations between attributes.
