Java Reference
In-Depth Information
Data Mining Process
To learn is to change. Education is a process that changes the learner.
—George B. Leonard, 1986
Historically, data mining has been viewed as the territory of gurus
and Ph.D.s and not for the techno-phobic or faint of heart. Fortu-
nately, increased understanding of the data mining process and
advances in automating many aspects of the traditional data mining
process are making data mining more accessible to mainstream
application developers. The data mining process involves learning
and, as the chapter-opening quotation notes, learning leads to
change—in the case of data mining, change in our understanding of
the business problem, change in our understanding of the data and
what it represents. To reap this understanding requires giving suffi-
cient thought to the problem to be solved as well as how to integrate
the data mining process and its results into the business process. The
ability to understand whether the data mining results meet the busi-
ness objectives and can be integrated with the business process are
key aspects of a successful corporate business intelligence strategy.
In this chapter, we characterize a rather complete and sophisticated
data mining process through the CRISP-DM standard, a popular data
mining process model, going from problem definition to solution
deployment [CRISP-DM 2006]. The standard largely approaches the
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