Using clustering to enhance text classification

Authors:
Antonia Kyriakopoulou;Theodore Kalamboukis
Affiliations:
Athens University of Economics and Business, Athens, Greece;Athens University of Economics and Business, Athens, Greece
Venue:
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2007

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Abstract

This paper addresses the problem of learning to classify textsby 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. Experiments conducted on several widely used datasets demonstrate the effectiveness of the proposed algorithm especially for small training sets.