LZW Compressed Text Classification using Nearest Neighbor Classifier

Internet is a pool of information, which contains billions of text documents which are stored in compressed format. In literature we can find many text classification algorithms which work on uncompressed text documents. In this paper, we propose a novel representation scheme for a given text document using compression technique. Further, proposed representation scheme is used to develop a methodology to classify the text documents. For the purpose of representation, we have used LZW compression technique and the dictionary representation obtained by LZW technique is used as representative for the text document. For classification we have used nearest neighbor method. Extensive experimentation is carried out on seven datasets, out of which three are our own datasets and remaining four are publically available datasets resulting with approximately 80% of F-measure.