Absolute Classification with Unsupervised Clustering

Abstract

An absolute classification algorithm is proposed in which the class definition through training samples or otherwise is required only for a particular class of interest. The absolute classification is considered as a problem of unsupervised clustering when one cluster is known initially. The definitions and statistics of the other classes are automatically developed through the weighted unsupervised clustering procedure, which is developed to keep the cluster corresponding to the class of interest from losing its identity as the class of interest. Once all the classes are developed, a conventional relative classifier such as the maximum-likelihood classifier is used in the classification.