  Partial classification popularly known as nugget discovery comes under
descriptive knowledge discovery. It involves mining rules for a target class of
interest. Classification "If-Then" rules are the most sought out by decision
makers since they are the most comprehensible form of knowledge mined by data
mining techniques. The rules have certain properties namely the rule metrics
which are used to evaluate them. Mining rules with user specified properties
can be considered as a multi-objective optimization problem since the rules
have to satisfy more than one property to be used by the user. Cultural
algorithm (CA) with its knowledge sources have been used in solving many
optimization problems. However research gap exists in using cultural algorithm
for multi-objective optimization of rules. In the current study a
multi-objective cultural algorithm is proposed for partial classification.
Results of experiments on benchmark data sets reveal good performance.
