TY  - JOUR
T1  - Integrating Ontology to Enhance HCL-Based Text Document Clustering
AU - Vijayalakshmi, S. AU - , D. Manimegalai 
JO  - Research Journal of Applied Sciences
VL  - 8
IS  - 7
SP  - 358
EP  - 368
PY  - 2013
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2013.358.368
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2013.358.368
KW  - BON
KW  -BOP
KW  -cosine similarity
KW  -ontology based K-Means
KW  -RiTa WordNet
KW  -ontology based NVK-means
KW  -ontology based HCLK-means
AB  - Increasingly large text datasets and the high dimensionality 
  associated with natural language is a great challenge of text mining. Initially, 
  researchers have been compared using three types of Document Representation 
  (Bag of Word (BoW), Bag of Noun (BoN) and Bag of Phrase (BoP)) and researchers 
  found that Bag of Noun and Bag of Phrase are performing better than BoW. BoP 
  significantly improves the better F-measure than BoN and BoW when the corpus 
  is smaller. If the corpus is larger, it increases the dimensionality. BoN document 
  representation working efficiently and also used to reduce its dimensionality 
  when the corpus is larger in text document clustering than BoP and BoN. Researchers 
  have been used Bag of Noun document representation. Nouns are checked with ontology 
  and extracted to construct term document matrix, although it reduces the dimension 
  and gives semantics. The comparative study result shows that the performance 
  of Bag of Noun document representation is better than Bag of Phrase. Exploration 
  of learning algorithm gives promising results in recent years. In this study, 
  researchers propose ontology based OHCLK-Means Clustering algorithm. It significantly 
  improves the clustering quality than ontology based K-means and ontology based 
  ONVK-means.
ER  -