A performance improvement methodology of web document clustering using FDC-TCT


The KIPS Transactions:PartD, Vol. 12, No. 4, pp. 637-646, Aug. 2005
10.3745/KIPSTD.2005.12.4.637,   PDF Download:

Abstract

There are various problems while applying classification or clustering algorithm in that document classification which requires post processing or classification after getting as a web search result due to any keyword. Among those, two problems are severe. The first problem is the need to categorize the document with the help of the expert. And, the second problem is the long processing time the document classification takes. Therefore we propose a new method of web document clustering which can dramatically decrease the number of times to calculate a document similarity using the Transitive Closure Tree(TCT) and which is able to speed up the processing without loosing the precision. We also compare the effectivity of the proposed method with those existing algorithms and present the experimental results.


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Cite this article
[IEEE Style]
S. B. Ko and S. D. Youn, "A performance improvement methodology of web document clustering using FDC-TCT," The KIPS Transactions:PartD, vol. 12, no. 4, pp. 637-646, 2005. DOI: 10.3745/KIPSTD.2005.12.4.637.

[ACM Style]
Suc Bum Ko and Sung Dae Youn. 2005. A performance improvement methodology of web document clustering using FDC-TCT. The KIPS Transactions:PartD, 12, 4, (2005), 637-646. DOI: 10.3745/KIPSTD.2005.12.4.637.