Sparse Web Data Analysis Using MCMC Missing Value Imputation and PCA Plot-based SOM 


Vol. 10,  No. 2, pp. 277-282, Apr.  2003
10.3745/KIPSTD.2003.10.2.277


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  Abstract

The knowledge discovery from web has been studied in many researches. There are some difficulties using web log for training data on efficient information predictive models. In this paper, we studied on the method to eliminate sparseness from web log data and to perform web user clustering. Using missing value imputation by Bayesian inference of MCMC, the sparseness of web data is removed. And web user clustering is performed using self organizing maps based on 3-D plot by principal component. Finally, using KDD Cup data, our experimental results were shown the problem solving process and the performance evaluation.

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[IEEE Style]

S. H. Jun and K. W. Oh, "Sparse Web Data Analysis Using MCMC Missing Value Imputation and PCA Plot-based SOM," The KIPS Transactions:PartD, vol. 10, no. 2, pp. 277-282, 2003. DOI: 10.3745/KIPSTD.2003.10.2.277.

[ACM Style]

Sung Hae Jun and Kyung Whan Oh. 2003. Sparse Web Data Analysis Using MCMC Missing Value Imputation and PCA Plot-based SOM. The KIPS Transactions:PartD, 10, 2, (2003), 277-282. DOI: 10.3745/KIPSTD.2003.10.2.277.

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