@inproceedings{Wong1999,
	title = {Visualizing association rules for text mining},
	author = {Pak Chung Wong and Paul Whitney and Jim Thomas},
	booktitle = {IEEE Symposium on Information Visualization},
	month = {October},
	pages = {120--123,152},
	url = {http://infoviz.pnl.gov/pdf/InfoVis1999Association.pdf},
	year = {1999},
	biburl = {http://www.bibsonomy.org/bibtex/2bfacc2101a989d980c000083bef87eae/marcoalvarez},
	abstract = {An association rule in data mining is an implication of the form X&
	where X is a set of antecedent items and Y is the consequent item.
	For years researchers have developed many tools to visualize association
	rules. However, few of these tools can handle more than dozens of
	rules, and none of them can effectively manage rules with multiple
	antecedents. Thus, it is extremely difficult to visualize and understand
	the association information of a large data set even when all the
	rules are available. This paper presents a novel visualization technique
	to tackle many of these problems. We apply the technology to a text
	mining study on large corpora. The results indicate that our design
	can easily handle hundreds of multiple antecedent association rules
	in a three-dimensional display with minimum human interaction, low
	occlusion percentage, and no screen swapping.},
	timestamp = {2007.09.26}, owner = {Marco},
	keywords = {AssocRules }
}