Rough Information Analysis Research Articles

Rough set data analysis (RSDA), introduced by Pawlak, has become a much researched method of knowledge discovery with over 1200 publications to date. One feature which distinguishes RSDA from other data analysis methods is that, in its original form, it gathers all its information from the given data, and does not make external model assumptions as all statistical and most machine learning methods (including decision tree procedures) do. The price which needs to be paid for the parsimony of this approach, however, is that some statistical backup is required, for example, to deal with random influences to which the observed data may be subjected. In supplementing RSDA by such meta-procedures care has to be taken that the same non-invasive principles are applied. In a sequence of papers and conference contributions, we have developed the components of a non-invasive method of data analysis, which is based on the RSDA principle, but is not restricted to “classical” RSDA applications. In this article, we present for the first time in a unified way the foundation and tools of such rough information analysis. © 2001 John Wiley & Sons, Inc.