Decoupling of Clustering and Classification Steps in a Cluster-Based Classification
Document Type
Contribution to Book
Publication Date
12-1-2005
Publication Title
International Conference on Machine Learning and Applications (ICMLA'05)
DOI
10.1109/ICMLA.2005.20
Abstract
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 disciminant analysis
Recommended Citation
Hashemi, Ray R., Mahmood Bahar, Charla R. Childers, Alexander Tyler.
2005.
"Decoupling of Clustering and Classification Steps in a Cluster-Based Classification."
International Conference on Machine Learning and Applications (ICMLA'05), M.A. Wani, M. Milanova, L. Kurgan, M. Reformat, & K. Hafeez (Ed.): 285-290 Los Angeles, CA.
doi: 10.1109/ICMLA.2005.20
https://digitalcommons.georgiasouthern.edu/compsci-facpubs/268
