Decoupling of clustering and classification steps in a cluster-based classification

The application of cluster analysis in the "classification" area is well known. Such application takes place in two steps: "clustering" and "classification". In the clustering step, the objects of a training set are clustered using a cluster technique, Q. The outcome is a set of clusters, C. Each cluster, ci, is assigned a class label, ki, which reflects the common features of the objects in ci. The ki is a member of set K In the classification step, a new object from a test set is assigned to one of the clusters in C using the Q, C, and K of the former step. The goal of this research effort is two fold: (1) introducing a methodology for decoupling "clustering" and "classification" steps and (2) establishing the validity of the proposed methodology by comparing its classification performance with the performance of the Rough Sets approach, and Desciminant Analysis.