01623nas a2200241   4500000000100000000000100001008004100002260001200043653001600055653001500071653001500086653002400101100002800125700001600153700001700169700001900186245007000205856009800275300001000373490000600383520097800389022001401367       2016                            d  c06/201610aData Mining10aClustering10aAlgorithms10aText Classification1 aAbdennour Mohamed Jalil1 aImad Hafidi1 aLamiae Alami1 aEnsa Khouribga00aComparative Study of Clustering Algorithms in Text Mining Context  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/05/ijimai20163_7_6_pdf_27159.pdf  a42-450 v33 aThe spectacular increasing of Data is due to the appearance of networks and 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 discovery databases, in order to treat textual data. This process is divided into three parts: preprocessing, processing and post-processing. In the processing step, we present a comparative study between several clustering algorithms such as KMeans, Global KMeans, Fast Global KMeans, Two Level KMeans and FWKmeans. The comparison between these algorithms is made on real textual data from the web using RSS feeds. Experimental results identified two problems: the first one quality results which remain for algorithms, which rapidly converge. The second problem is due to the execution time that needs to decrease for some algorithms.  a1989-1660