In this paper, we examine the performance of four fuzzy rule generation methods on Wisconsin breast cancer data. The first
method generates fuzzy if-then rules using the mean and the standard deviation of attribute values. The second approach generates
fuzzy if-then rules using the histogram of attributes values. The third procedure generates fuzzy if-then rules with certainty of
each attribute into homogeneous fuzzy sets. In the fourth approach, only overlapping areas are partitioned. The first two
approaches generate a single fuzzy if-then rule for each class by specifying the membership function of each antecedent fuzzy set
using the information about attribute values of training patterns. The other two approaches are based on fuzzy grids with
homogeneous fuzzy partitions of each attribute. The performance of each approach is evaluated on breast cancer data sets.
Simulation results show that the Modified grid approach has a high classification rate of 99.73 %.