  The importance of finding the characteristics leading to either a success or
a failure is one of the driving forces of data mining. The various application
areas of finding success/failure factors cover vast variety of areas such as
credit risk evaluation and granting loans, micro array analysis, health factors
and health risk factors, and parameter combination leading to a product
success. This paper presents a new approach for making inferences about
dichotomous data. The objective is to determine rules that lead to a certain
result. The method consists of four phases: in the first phase, the data is
processed into a binary format of a truth table, in the second phase; rules are
found by utilizing an algorithm that minimizes Boolean functions. In the third
phase the rules are checked and filtered. In the fourth phase, simple rules
that involve one to two features are revealed.
