Mining pure linguistic associations from numerical data

This paper contains a method for direct search of associations from numerical data that are expressed in natural language and so, we call them "linguistic associations". The associations are composed of evaluative linguistic expressions, for example "small, very big, roughly medium", etc. The main idea is to evaluate real-valued data by the corresponding linguistic expressions and then search for associations using some of the standard data-mining technique (we have used the GUHA method). One of essential outcomes of our theory is high understandability of the found associations because when formulated in natural language they are much closer to the way of thinking of experts from various fields. Moreover, associations characterizing real dependencies can be directly taken as fuzzy IF-THEN rules and used as expert knowledge about the problem. (C) 2007 Elsevier Inc. All rights reserved.