Comparative Study of Clustering Algorithms in Text Mining Context

Abstract

The spectacular increasing of Data is due to the appearance of networksand smartphones. Amount 42% of world population using internet {[}1];have created a problem related of the processing of the data exchanged,which is rising exponentially and that should be automatically treated.This paper presents a classical process of knowledge discoverydatabases, in order to treat textual data. This process is divided intothree parts: preprocessing, processing and post-processing. In theprocessing step, we present a comparative study between severalclustering algorithms such as KMeans, Global KMeans, Fast Global KMeans,Two Level KMeans and FWKmeans. The comparison between these algorithmsis made on real textual data from the web using RSS feeds. Experimentalresults identified two problems: the first one quality results whichremain for algorithms, which rapidly converge. The second problem is dueto the execution time that needs to decrease for some algorithms.