# Condor

The objective of the Condor project is
the development and implementation of an integrative methodical
platform for the processing of different kinds of uncertain
knowledge, e.g. qualitative, probabilistic and semi-quantitative
knowledge. This knowledge can be utilized for different tasks like
knowledge discovery, inference and revision. Condor is based on a
new concept for representing *conditionals*
(*if*-*then*-rules), in which conditionals are
interpreted as agents acting on so called *possible
worlds*.

### Funding

DFG – Deutsche Forschungsgemeinschaft (BE 1700/5)

## Summary

The main focus of the Condor project is
a novel, theoretically sound algorithm for knowledge discovery from
in databases (*KDD*). In a very general sense, the aim of
knowledge discovery is to reveal the *structures of
knowledge*, in our approach assumed to be representable by
conditionals. There are two key ideas underlying the approach
pursued by our algorithm: First, knowledge discovery is understood
as a process which is inverse to inductive knowledge
representation. So the relevance of discovered information is judged
with respect to the chosen representation method, in our case
the *principle of maximum entropy* (*ME*). This way
the discovered rules can be considered as being most informative in
a strict, formal sense. Second, the link between structural and
numerical knowledge is established by an algebraic theory of
conditionals, which considers conditionals as agents acting on
possible worlds.

*Knowledge discovery seen as inverse to inductive
reasoning.*

## CondorCKD

Our CondorCKD-algorithm
(*conditional knowledge discovery*, *CKD*) utilizes a
*bottom-up approach* for computing the set of most
informative rules from a database. The input data is assumed to be a
discrete multivariate distribution, which can be read from a CSV or
ARFF file. The algorithm starts with conditionals with
single-elementary conclusions and long premises, and shortens theses
premises to make the conditionals most expressive but without losing
information, in accordance with the information inherent to the
data.

The whole CondorCKD-algorithm is implemented in the functional programming language Haskell, including a graphical user interface, and runs on Windows and Linux. It takes data in the form of CSV or ARFF files (these are formats for exchanging tabular data widely used in data mining systems or spreadsheet software) as input, and can output the computed rules in a simple text format (ready for further processing) as well as in a polished LaTeX format.

*Overview of the Condor
system.*

## International Workshops

- Workshop on Conditionals, Information and Inference 2002 (CII '02)
- Workshop on Conditionals, Information and Inference 2004 (CII '04)