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In the area of homeland security, data mining is often met with
skepticism. There are concerns over privacy and accuracy. In particu-
lar, Seifert [2004] notes,
One limitation is that although data mining can help reveal patterns and relation-
ships, it does not tell the user the value or significance of these patterns. These types of
determinations must be made by the user. A second limitation is that while data
mining can identify connections between behaviors and/or variables, it does not
necessarily identify a causal relationship. To be successful, data mining still requires
skilled technical and analytical specialists who can structure the analysis and
interpret the output that is created.
Despite these limitations, data mining is being used for identifying
terrorists and for analyzing large volumes of text documents, includ-
ing the Web and e-mail, for possible breaches in national security.
In lottery systems, data mining is employed to increase revenues
by predicting customer color or game preferences, to acquire and
retain customers, and to determine in which regions certain games
are most successful [SPSS 2005]. Attribute importance can be used to
determine which customer demographics most affect game success.
Classification techniques can predict which customers are likely to
prefer certain types of games.
The communications industry is one of intense competition. Many of
the cross-industry problems noted above apply: customer retention/
attrition referred to as “churn,” response modeling, fraud detection,
and cross-sell. As noted in Peppers/Rogers [1999],
GTE developed a data mining product called ChurnManager that scans all the data
in a customer's file and summarizes it in an easy-to-use graphical interface that is
prominent on the very first customer record screen displayed to the service rep
answering the call. / Every customer's relationship with GTE is summarized … to pro-
vide [the customer service representatives] with instant notification of potential cus-
tomer dissatisfaction, as well as customer value and vulnerability to leaving the service.
Another specific communications industry problem is in the area
of network performance management.
A leading US operator uses [data mining] to ensure that calls are routed effectively.
This is done by continuous monitoring of performance rules and the analysis of data,
both data on the history of component and trunk usage, and on the current network
activity metrics. This operator has seen “false” service and engineering call-outs
decrease and the number of successful calls on their network increase. [Morgan 2003]
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