Refereed full papers (journals, book chapters, international conferences)

2001

@inproceedings{YaZ2001,
	vgclass =	{refpap},
	author =	{Jinsan Yang and Byoung-Tak Zhang},
	title =	{Customer Data Mining and Visualization by Generative
	Topographic Mapping Methods},
	editor =	{Simeon J. Simoff and Monique Noirhomme-Fraiture and
	Michael H. B\"{o}hlen},
	booktitle =	{Proceedings of the International Workshop on Visual Data
	Mining (VDM@ECML/PKDD2001)},
	address =	{Freiburg, Germany},
	pages =	{55--66},
	month =	{4~September},
	year =	{2001},
	url =	{http://www-staff.it.uts.edu.au/\~{}simeon/vdm_pkdd2001/web_proceedings/05_yang.pdf},
	abstract =	{Understanding various characteristics of potential
	customers is important in the web business with respect to economy and
	efficiency. When analyzing a large data set on the customers, the
	structure of data can be highly complicated due to the correlations and
	redundancy of the observed data. In that case a meaningful insight of
	data can be discovered by applying a latent variable model to the
	observed data. Generative topographic mapping (GTM) [2] is a latent
	graphical model which can simplify the data structure by projecting a
	high dimensional data onto a lower dimensional space of intrinsic
	features. When the latent space is a plane, we can visualize the data
	set in the latent plane. We applied GTM methods in analyzing the web
	customer data and compared their relative merits on the clustering and
	visualization with other known method like self-organizing map (SOM)
	[10] or principle component projections (PCA). When applied to a KDD
	data set, GTM demonstrated improved visualizations due to its
	probabilistic and nonlinear mapping.},
}