Abstract
This paper addresses the problem of learning to classify texts by exploiting information derived from clustering both training and testing sets. The incorporation of knowledge resulting from clustering into the feature space representation of the texts is expected to boost the performance of a classifier. We present an empirical study of the proposed algorithm on a variety of datasets. We attempt to answer various questions such as the effect of independence or relevance amongst features, the effect of the size of the training set and the effect of noise. The results are encouraging, revealing that information resulting from clustering can create text classifiers of high-accuracy.
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