Text mining with hybrid biclustering algorithms

Abstract

Text data mining is the process of extracting valuable information from a dataset consisting of text documents. Popular clustering algorithms do not allow detection of the same words appearing in multiple documents. Instead, they discover general similarity of such documents. This article presents the application of a hybrid biclustering algorithm for text mining documents collected from Twitter and symbolic analysis of knowledge spreadsheets. The proposed method automatically reveals words appearing together in multiple texts. The proposed approach is compared to some of the most recognized clustering algorithms and shows the advantage of biclustering over clustering in text mining. Finally, the method is confronted with other biclustering methods in the task of classification.