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Chapter 9 Business Intelligence Systems
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Q9-5 How Do Organizations Use Data Mining
Applications?

Data mining is the application of statistical techniques to find patterns and relationships among
data for classification and prediction. As shown in Figure 9-20, data mining resulted from a con-
vergence of disciplines. Data mining techniques emerged from statistics and mathematics and
from artificial intelligence and machine-learning fields in computer science. As a result, data min-
ing terminology is an odd blend of terms from these different disciplines. Sometimes people use the
term knowledge discovery in databases (KDD) as a synonym for data mining.
Data mining and other business Most data mining techniques are sophisticated, and many are difficult to use well. Such
intelligence systems are useful, but techniques are valuable to organizations, however, and some business professionals, especially
they are not without their problems, those in finance and marketing, have become expert in their use. In fact, today there are many
as discussed in the Guide on pages interesting and rewarding careers for business professionals who are knowledgeable about data
408–409.
mining techniques.
Data mining techniques fall into two broad categories: unsupervised and supervised. We
explain both types in the following sections.

Unsupervised Data Mining

With unsupervised data mining, analysts do not create a model or hypothesis before run-
ning the analysis. Instead, they apply a data mining application to the data and observe the
results. With this method, analysts create hypotheses after the analysis, in order to explain the
patterns found.
One common unsupervised technique is cluster analysis. With it, statistical techniques
identify groups of entities that have similar characteristics. A common use for cluster analysis is
to find groups of similar customers from customer order and demographic data.
For example, suppose a cluster analysis finds two very different customer groups: One
group has an average age of 33, owns four Android phones and three iPads, has an expensive
home entertainment system, drives a Lexus SUV, and tends to buy expensive children’s play
equipment. The second group has an average age of 64, owns Arizona vacation property, plays
golf, and buys expensive wines. Suppose the analysis also finds that both groups buy designer
children’s clothing.
These findings are obtained solely by data analysis. There is no prior model about the patterns
and relationships that exist. It is up to the analyst to form hypotheses, after the fact, to explain why
two such different groups are both buying designer children’s clothes.

Statistics/ Artificial Intelligence
Mathematics Machine Learning

Huge Data
Databases Mining

Sophisticated
Cheap Computer Marketing, Finance, Data
Processing and and Other Business Management
Figure 9-20 Storage Professionals Technology
Source Disciplines of Data Mining