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Data Mining for Customer Relationship Management
(CRM)
Data Mining has enjoyed great popularity in recent
years, with advances
in both research and commercialization. The first generation of data mining
research and development has yielded several commercially available systems,
both stand-alone and integrated with database systems; produced scalable
versions of algorithms for many classical data mining problems; and
introduced novel pattern discovery problems.
In recent years, research has tended to be fragmented into several distinct
pockets without a comprehensive framework. Researchers have continued to
work largely within the parameters of their parent disciplines, building
upon existing and distinct research methodologies. Even when they address
a common problem (for example, how to cluster a dataset) they apply different
techniques, different perspectives on what the important issues are, and
different evaluation criteria. While different approaches can be
complementary, and such a diversity is ultimately a strength of the
field, better communication across disciplines is required if Data
Mining is to forge a distinct identity with a core set of principles,
perspectives, and challenges that differentiate it from each of the
parent disciplines. Further, while the amount and complexity of data
continues to grow rapidly, and the task of distilling useful insight
continues to be central, serious concerns have emerged about social
implications of data mining. Addressing these concerns will require
advances in our theoretical understanding of the principles that underlie
Data Mining algorithms, as well as an integrated approach to security and
privacy in all phases of data management and analysis.
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