@conference {13480,
	title = {Visualization of high-dimensional model characteristics},
	booktitle = {Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management},
	series = {NPIVM {\textquoteright}99},
	year = {1999},
	month = {1999///},
	pages = {6 - 8},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Using inductive learning techniques to construct explanatory models for large, high-dimensional data sets is a useful way to discover useful information. However, these models can be difficult for users to understand. We have developed a set of visualization methods that enable a user to evaluate the quality of learned models, to compare alternative models, and identify ways in which a model might be improved We describe the visualization techniques we have explored, including methods for high-dimensional data space projection, variable/class correlation, instance mapping, and model sampling We show the results of applying these techniques to several models built from a benchmark data set of census data.},
	keywords = {data mining and knowledge discovery, multidimensional information spaces, Visualization},
	isbn = {1-58113-254-9},
	doi = {10.1145/331770.331774},
	url = {http://doi.acm.org/10.1145/331770.331774},
	author = {desJardins, Marie and Rheingans,Penny}
}
