%0 Journal Article
%T IMPLEMENTATION OF KEA-KEYPHRASE EXTRAC-TION ALGORITHM BY USING BISECTING K-MEANS CLUSTERING TECHNIQUE FOR LARGE AND DYNAMIC DATA SET
%A M. Arshad
%J International Journal of Advanced Technology & Engineering Research
%D 2012
%I 
%X In most traditional techniques of document clustering, the number of total clusters is not known in advance and the cluster that contains the target information cannot be deter-mined since the semantic nature is not associated with the cluster. To solve this problem, this work proposes a new clustering algorithm based on the Kea[1] key phrase extrac-tion algorithm which returns several key phrases from the source documents by using some machine learning tech-niques. In this work, documents are grouped into several clusters like Bisecting K-means, but the number of clusters is automatically determined by the algorithm with some heu-ristics using the extracted key phrases. By this it is easy to extract test documents from massive quantities of resources.
%K KEA
%K K-means
%K Bisecting K-means
%K Clusters
%K Key Phrase Extraction
%U http://www.ijater.com/Files/62e1cf11-728c-4286-a239-4a9fe90682ab_IJATER_03_27.pdf