2001
@inbook{TBD2001,
vgclass = {refpap},
author = {Kurt Thearling and Barry Becker and Dennis DeCoste and
Bill Mawby and Michel Pilote and Dan Sommerfield},
title = {Visualizing Data Mining Models},
editor = {Usama M. Fayyad and Georges G. Grinstein and Andreas Wierse},
booktitle = {Information Visualization in Data Mining and Knowledge
Discovery},
chapter = {15},
pages = {205--222},
publisher = {Morgan Kaufmann},
year = {2001},
url = {http://www.thearling.com/text/dmviz/modelviz.htm},
abstract = {The point of data visualization is to let the user
understand what is going on. Since data mining usually involves
extracting "hidden" information from a database, this understanding
process can get somewhat complicated. In most standard database
operations nearly everything the user sees is something that they knew
existed in the database already. A report showing the breakdown of
sales by product and region is straightforward for the user to
understand because they intuitively know that this kind of information
already exists in the database. If the company sells different products
in different regions of the county, there is no problem translating a
display of this information into a relevant understanding of the
business process.
Data mining, on the other hand, extracts information from a database
that the user did not already know about. Useful relationships between
variables that are non-intuitive are the jewels that data mining hopes
to locate. Since the user does not know beforehand what the data
mining process has discovered, it is a much bigger leap to take the
output of the system and translate it into an actionable solution to a
business problem. Since there are usually many ways to graphically
represent a model, the visualizations that are used should be chosen to
maximize the value to the viewer. This requires that we understand the
viewer's needs and design the visualization with that end-user in mind.
If we assume that the viewer is an expert in the subject area but not
data modeling, we must translate the model into a more natural
representation for them. For this purpose we suggest the use of
orienteering principles as a template for our visualizations.},
}