Abstract: The fuzzy k-means-based fuzzy classifier combines clustering of fuzzy k-means algorithm with a fuzzy rule tractor.We propose to efficiently design a fuzzy classifier so that the training patterns can be correctly classified.This method follows the principle of partitioning and covering technique.The fuzzy k-means algorithm is first used to partition the training data for each class into several clusters,and the cluster center and the radius for each cluster are calculated.A fuzzy system design method that uses a fuzzy rule to represent a cluster is then proposed so that a fuzzy classifier can be efficiently constructed to correctly classify the training data.The proposed method does not need a prior parameter definition,but only needs a short training time,therefore is simple.