Representing Data Mining Results

Abstract

How many different ways can we represent our data in order to convey its meaning to its intended audience? What is the most effective approach? Is one technique better than another? The answer depends on your data, your audience, and the message you are trying to convey. In this chapter, we provide context for the output of our analyses, discussing very traditional representation methodologies, including tables, histograms, graphs, and plots of various types, as well as some of the more creative approaches that have seen increasing mindshare as vehicles of communication across the Internet. We also include an important technique that spans both the algorithmic and representation concepts – trees and rules – since such techniques can be valuable for both explanation and input to other systems and show that not all representations necessarily need to be graphical.