Berry

Cumulative Proportion
The Lure of Statistics: Data Mining Using Familiar Tools 127
Looking at Discrete Values
Much of the data used in data mining is discrete by nature, rather than continuous.
Discrete data shows up in the form of products, channels, regions, and
descriptive information about businesses. This section discusses ways of looking
at and analyzing discrete fields.
Histograms
The most basic descriptive statistic about discrete fields is the number of
times different values occur. Figure 5.1 shows a histogram of stop reason codes
during a period of time. A histogram shows how often each value occurs in the
data and can have either absolute quantities (204 times) or percentage (14.6
percent). Often, there are too many values to show in a single histogram such
as this case where there are over 30 additional codes grouped into the “other”
category.
In addition to the values for each category, this histogram also shows the
cumulative proportion of stops, whose scale is shown on the left-hand side.
Through the cumulative histogram, it is possible to see that the top three codes
account for about 50 percent of stops, and the top 10, almost 90 percent. As an
aesthetic note, the grid lines intersect both the left- and right-hand scales at
sensible points, making it easier to read values off of the chart.
12,500
100%
10,000
10,048
80%
Number of Stops
7,500
5,000
5,944
3,851
3,549
3,311
3,054
4,884
60%
40%
2,500
20%
1,491 1,306 1,226 1,108
0
0%
TI NO OT VN PE CM CP NR MV EX
OTHER
Stop Reason Code
Figure 5.1 This example shows both a histogram (as a vertical bar chart) and cumulative
proportion (as a line) on the same chart for stop reasons associated with a particular
marketing effort.