Kurt Thearling, Barry Becker, Dennis DeCoste, Bill Mawby, Michel Pilote and Dan Sommerfield,
Visualizing Data Mining Models,
In Usama M. Fayyad, Georges G. Grinstein and Andreas Wierse eds., Information Visualization in Data Mining and Knowledge
Discovery,
15, pp. 205-222, Morgan Kaufmann, 2001.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.