Most of the earlier work on clustering mainly focused on numeric data whose inherent geometric properties can be exploited to naturally define distance functions be-tween data points. However, data mining applications frequently involve many datasets that also consists of mixed numeric and categorical attributes. In this paper we present a clustering algorithm which is based on the fc-means algorithm. The algorithm clusters objects with numeric and categorical attributes in a way similar to Avmeans. The object similarity measure is derived from both numeric and categorical attributes. When applied to numeric data, the algorithm is identical to the &-means. The main result of this paper is to provide a method to update the "cluster centers" of clustering objects described by mixed numeric and categorical attributes in the clustering process to minimize the cluster-ing cost function. The clustering performance of the algorithm is demonstrated with the two well known daLa sets, namely credit approval and abalone databases.