Authors 
Title 
Status, Abstract 

Gunther Schmidt 
Theory Extraction in Relational Data Analysis 
Has appeared as contribution pp. 6886 to 

University of the Federal Armed Forces Munich 85577 Neubiberg, Germany Gunther.Schmidt@unibw.de 

From numerical mathematics we know that a linear equation Ax = b may be
solved more efficiently if a reduction of A is known beforehand.
Having an a priori knowledge of this kind is also an advantage in many
other application fields. We here deal with a diversity of techniques to decompose
relations according to some criteria and embed these techniques in a common
framework.
The results of decompositions obtained may be used in decision making, but also as a support for teaching, as they often give visual help. Our starting point will always be a concretely given relation, i.e., a Boolean matrix. In most cases, we will look for a partition of the set of rows and the set of columns, respectively, that arises from some algebraic condition. From these partitions, a rearranged matrix making these partitions easily visible shall be computed as well as the permutation matrix necessary to achieve this. The current article presents results of a report, which gives a detailed account of the topic. The report is not just a research report but also a Haskell program in literate style. In contrast, the present article only gives hints as to these programs. Therefore, some details are omitted. This article is organized as follows. 