Latent Semantic Indexing Analysis of K-Means Document Clustering for Changing Index Terms Weighting


The KIPS Transactions:PartB , Vol. 10, No. 7, pp. 735-742, Dec. 2003
10.3745/KIPSTB.2003.10.7.735,   PDF Download:

Abstract

In the information retrieval system, document clustering technique is to provide user convenience and visual effects by rearranging documents according to the specific topics from the retrieved ones. In this paper, we clustered documents using K-Means algorithm and present the effect of index terms weighting scheme on the document clustering. To verify the experiment, we applied Latent Semantic Indexing approach to illustrate the clustering results and analyzed the clustering results in 2-dimensional space. Experimental results showed that in case of applying local weighting, global weighting and normalization factor, the density of clustering is higher than those of similar or same weighting schemes in 2-dimensional space. Especially, the logarithm of local and global weighting is noticeable.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
O. H. Jin, G. J. Hyeon, A. D. Eon, P. S. Cheol, "Latent Semantic Indexing Analysis of K-Means Document Clustering for Changing Index Terms Weighting," The KIPS Transactions:PartB , vol. 10, no. 7, pp. 735-742, 2003. DOI: 10.3745/KIPSTB.2003.10.7.735.

[ACM Style]
O Hyeong Jin, Go Ji Hyeon, An Dong Eon, and Park Sun Cheol. 2003. Latent Semantic Indexing Analysis of K-Means Document Clustering for Changing Index Terms Weighting. The KIPS Transactions:PartB , 10, 7, (2003), 735-742. DOI: 10.3745/KIPSTB.2003.10.7.735.