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2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)
A Combined Approach of Formal Concept Analysis and Text Mining for Concept Based Document Clustering
Compi?gne University of Technology, France
September 19-September 22
ISBN: 0-7695-2415-X
ASCII Text
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Nyeint Nyeint Myat, Khin Haymar Saw Hla, "A Combined Approach of Formal Concept Analysis and Text Mining for Concept Based Document Clustering," Web Intelligence, IEEE / WIC / ACM International Conference on, pp. 330-333, 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005.
 
 
BibTex
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@article{ 10.1109/WI.2005.1,
author = {Nyeint Nyeint Myat and Khin Haymar Saw Hla},
title = {A Combined Approach of Formal Concept Analysis and Text Mining for Concept Based Document Clustering},
journal ={Web Intelligence, IEEE / WIC / ACM International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2415-X},
pages = {330-333},
doi = {http://doi.ieeecomputersociety.org/10.1109/WI.2005.1},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Web Intelligence, IEEE / WIC / ACM International Conference on
TI - A Combined Approach of Formal Concept Analysis and Text Mining for Concept Based Document Clustering
SN - 0-7695-2415-X
SP330
EP333
A1 - Nyeint Nyeint Myat,
A1 - Khin Haymar Saw Hla,
PY - 2005
KW - conceptual document clustering
KW - formal concept analysis (FCA)
KW - frequent termsets
KW - association
KW - correlation
VL - 0
JA - Web Intelligence, IEEE / WIC / ACM International Conference on
ER -
 
Nyeint Nyeint Myat, University of Computer Studies
Khin Haymar Saw Hla, University of Computer Studies
Nowadays, the demand of conceptual document clustering is becoming increase to manage various types of vast amount of information published on the World Wide Web. In this paper, we use Formal Concept Analysis (FCA) method for clustering documents according to their formal contexts. Concept hierarchy of documents is built using the formal concepts of the documents in the document corpus. We use tf.idf (term frequency × inverse document frequency) term weighting model to reduce less useful concepts from these formal concepts and the association and correlation mining techniques to analyze the relationship of terms in the document corpus.
Index Terms:
conceptual document clustering, formal concept analysis (FCA), frequent termsets, association, correlation
Citation:
Nyeint Nyeint Myat, Khin Haymar Saw Hla, "A Combined Approach of Formal Concept Analysis and Text Mining for Concept Based Document Clustering," wi, pp.330-333, 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005
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