Preface
We have been watching an explosive growth of application of Data Mining (DM) tech-
nologies in an increasing number of different areas of business, government and science.
Two of the most important business areas are finance, in particular in banks and insur-
ance companies, and e-business, such as web portals, e-commerce and ad management
services.
In spite of the close relationship between research and practice in Data Mining, it
is not easy to find information on some of the most important issues involved in real
world application of DM technology, from business and data understanding to evaluation
and deployment. Papers often describe research that was developed without taking into
account constraints imposed by the motivating application. When these issues are taken
into account, they are frequently not discussed in detail because the paper must focus on
the method. Therefore, knowledge that could be useful for those who would like to apply
the same approach on a related problem is not shared.
In 2007, we organized a workshop with the goal of attracting contributions that
address some of these issues. The Data Mining for Business workshop was held to-
gether with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD), in Nanjing, China.
1
This book contains extended versions of a selection of papers from that workshop.
Due to the importance of the two application areas, we have selected papers that are
mostly related to finance and e-business. The chapters of this book cover the whole range
of issues involved in the development of DM projects, including the ones mentioned ear-
lier, which often are not described. Some of these papers describe applications, includ-
ing interesting knowledge on how domain-specific knowledge was incorporated in the
development of the DM solution and issues involved in the integration of this solution
in the business process. Other papers illustrate how the fast development of IT, such as
blogs or RSS feeds, opens many interesting opportunities for Data Mining and propose
solutions to address them.
These papers are complemented with others that describe applications in other im-
portant and related areas, such as intrusion detection, economic analysis and business
process mining. The successful development of DM applications depends on methodolo-
gies that facilitate the integration of domain-specific knowledge and business goals into
the more technical tasks. This issue is also addressed in this book.
This book clearly shows that Data Mining projects must not be regarded as inde-
pendent efforts but they should rather be integrated into broader projects that are aligned
with the company’s goals. In most cases, the output of DM projects is a solution that must
be integrated into the organization’s information system and, therefore, in its (decision-
making) processes.
Additionally, the book stresses the need for DM researchers to keep up with the pace
of development in IT technologies, identify potential applications and develop suitable
1
http://www.liaad.up.pt/dmbiz.
Applications of Data Mining in E-Business and Finance
C. Soares et al. (Eds.)
IOS Press, 2008
© 2008 The authors and IOS Press. All rights reserved.
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