@article{(Open Science Index):https://publications.waset.org/pdf/631,
	  title     = {Ontology-based Concept Weighting for Text Documents},
	  author    = {Hmway Hmway Tar and  Thi Thi Soe Nyaunt},
	  country	= {},
	  institution	= {},
	  abstract     = {Documents clustering become an essential technology
with the popularity of the Internet. That also means that fast and
high-quality document clustering technique play core topics. Text
clustering or shortly clustering is about discovering semantically
related groups in an unstructured collection of documents. Clustering
has been very popular for a long time because it provides unique
ways of digesting and generalizing large amounts of information.
One of the issues of clustering is to extract proper feature (concept)
of a problem domain. The existing clustering technology mainly
focuses on term weight calculation. To achieve more accurate
document clustering, more informative features including concept
weight are important. Feature Selection is important for clustering
process because some of the irrelevant or redundant feature may
misguide the clustering results. To counteract this issue, the proposed
system presents the concept weight for text clustering system
developed based on a k-means algorithm in accordance with the
principles of ontology so that the important of words of a cluster can
be identified by the weight values. To a certain extent, it has resolved
the semantic problem in specific areas.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {5},
	  number    = {9},
	  year      = {2011},
	  pages     = {991 - 995},
	  ee        = {https://publications.waset.org/pdf/631},
	  url   	= {https://publications.waset.org/vol/57},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 57, 2011},
	}