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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.
Helpful Books
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.
See author's bio and more book details >>
In this introduction to data mining, authors Michael J. A. Berry and Gordon S. Linoff – leading authorities on the use of data mining techniques for business applications – present complex data mining concepts clearly and concisely. If you’re looking to apply data mining techniques to your company’s marketing, sales and CRM efforts, start here.
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 – a learning method particularly suited to financial mining – and present an overview of major algorithmic approaches to predictive data mining, as well as their application within the financial services industry.
The following resources have been compiled with a view to helping you get the most from your data mining practices.
Helpful Books
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 mining.
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 – leading authorities on the use of data mining techniques for business applications – present complex data mining concepts clearly and concisely. If you’re looking to apply data mining techniques to your company’s marketing, sales and CRM efforts, start here.
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 – a learning method particularly suited to financial mining – 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.
