  In this paper, we present a semi-supervised learning algorithm for
classification of text documents. A method of labeling unlabeled text documents
is presented. The presented method is based on the principle of divide and
conquer strategy. It uses recursive K-means algorithm for partitioning both
labeled and unlabeled data collection. The K-means algorithm is applied
recursively on each partition till a desired level partition is achieved such
that each partition contains labeled documents of a single class. Once the
desired clusters are obtained, the respective cluster centroids are considered
as representatives of the clusters and the nearest neighbor rule is used for
classifying an unknown text document. Series of experiments have been conducted
to bring out the superiority of the proposed model over other recent state of
the art models on 20Newsgroups dataset.
