Welcome to the Resource Centre
The following resources have been compiled with a view to helping you get the most from your data mining practices.
Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner,
published by SAS Press. Using SAS Enterprise Miner, this book
illustrates the application and operation of decision trees in business
intelligence, data mining, business analytics, prediction, and
knowledge discovery. It explains in detail the use of decision trees as
a data mining technique and how this technique complements and
supplements data mining approaches such as regression, as well as other
business intelligence applications that incorporate tabular reports,
OLAP, or multidimensional cubes. Examples show how various aspects of
decision trees are constructed, how they operate, how to interpret
them, and how to use them in a range of predictive and descriptive
applications. The examples are drawn from the areas of purchase
behavior, risk assessment, and business-to-business marketing.
This book also describes the various
disciplines that contributed to the development of decision trees and
how, even today, decision trees can be used as a form of machine
intelligence. Examples of using and interpreting graphic decision trees
as executable rules are provided.
The target audience includes
analysts who have an introductory understanding of data mining and who
want to benefit from a more advanced, in-depth look at the theory and
methods of a decision tree approach to business intelligence and data
See author's bio and more book details >>
Data Mining Techniques, Second Edition, published by Wiley Publishing, Inc.
In this introduction to data mining, authors Michael J. A. Berry and
Gordon S. Linoff â€“ leading authorities on the use of data mining
techniques for business applications â€“ present complex data mining
concepts clearly and concisely. If youâ€™re looking to apply data mining
techniques to your companyâ€™s marketing, sales and CRM efforts, start
Data Mining in Finance: Advances in Relational and Hybrid Methods, published by Springer
In this first book to be dedicated to data mining in the financial
services industry, authors Boris Kovalerchuk and Evgenii Vityaev
explore relational data mining â€“ a learning method particularly suited
to financial mining â€“ and present an overview of major algorithmic
approaches to predictive data mining, as well as their application
within the financial services industry.
Investigative Data Mining for Security and Criminal Detection,
published by Butterworth-Heinemann. Author Jesus Mena explores how the
latest data mining techniques can be used as investigative tools. A
valuable resource for software developers and vendors in the security
industry, the book captures real-world applications of data mining
through screen captures, case studies and diagrams.
Asset Liability Management of Financial
Institutions: Maximising Shareholder Value through Risk-Conscious
Investing, published by Euromoney Books. Edited by Leo M. Tilman. The
first-ever definitive guide to Asset/Liability Management (ALM) across
the spectrum of financial institutions, this book is essential in
developing consistent frameworks for risk management. Leveraging the
experience of 38 senior industry practitioners, it provides a unique
and practical perspective on the practice of ALM.