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Introduction to Neural Networks and Data Mining for Business Applications
Kate Smith graduated from the University of Melbourne with a B.Sc.(Hons) in 1993 and a Ph.D. in 1996. As a Senior Lecturer in the School of Business Systems at Monash University in Melbourne, her research involved the development of neural network, data mining and more traditional techniques for solving practical problems from business and industry. She has been involved in many industry funded projects and regularly acts as a consultant in these areas. She also runs industry short courses on neural networks and data mining. She has published extensively in international journals and conference proceedings, and is a referee for many quality journals in the field. Her international activities, including memberships of conference organising committees and guest editing of journals, have given her an increasing international profile.
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Introduction to Neural Networks and Data Mining
Eruditions Publishing, Melbourne, 1998.
Papercover, 160 pages, 175 mm x 245 mm.
ISBN 978-186491-004-9 RRP AU$ 59.95
[Postage/Handling: Within Australia AU$15; International, AU$25 Air Mail] -
Description
Neural networks are a hot topic in the business community today. Also marketed as intelligent techniques, business intelligence and data mining, many businesses are now realising the potential of neural networks to give them a competitive edge. Nevertheless most neural network books are written by electrical engineers for electrical engineers, with a high level of mathematics. Those few books aimed at the business community invariably focus exclusively on financial prediction.
Consequently, Introduction to Neural Networks and Data Mining for Business Applications is a ground breaking text. With a minimum of mathematics, it shows the potential of neural networks to unlock hidden information in data of various industries including retail, marketing, insurance, telecommunications, banking and finance, and operations management.
The book covers the development of neural network research and its impact on business; the early neural Perceptron model and its limitations; backpropagation, the most commonly used learning paradigm in business applications; self-organisation; and adaptive resonance theory. Data mining is then covered including the purpose, methodology, and concepts of directed and undirected knowledge discovery. Other intelligent techniques often used in conjunction with neural networks are also covered, including genetic algorithms, fuzzy logic, and expert systems. The text concludes with a discussion of the future of neural networks research and applications. Extensive business case studies are used throughout the text to demonstrate techniques.
Suited for the following courses: ideal as a text for both undergraduate and postgraduate courses in neural networks and data mining. For business, commerce and computing students.
Buy this book now with your Credit Card:
Introduction to Neural Networks and Data Mining
Eruditions Publishing, Melbourne, 1998.
Papercover, 160 pages, 175 mm x 245 mm.
ISBN 978-186491-004-9 RRP AU$ 59.95
[Postage/Handling: Within Australia AU$15; International, AU$25 Air Mail]
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Contents
- Introduction
- Perceptrons
- Multilayered Networks
- Case Studies
- Self-Organisation
- Data Mining
- Data Mining Case Studies
- Other Intelligent Techniques
- Conclusion
- Bibliography Appendices Index
Buy this book now with your Credit Card:
Introduction to Neural Networks and Data Mining
Eruditions Publishing, Melbourne, 1998.
Papercover, 160 pages, 175 mm x 245 mm.
ISBN 978-186491-004-9 RRP AU$ 59.95
[Postage/Handling: Within Australia AU$15; International, AU$25 Air Mail]
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Key Features
- Aimed at business students, not at students of electrical engineering as most other books in the field
- Techniques are applied to solving business problems throughout the text
- Includes case studies and examples from retail, insurance, telecommunications, banking and finance
- Provides a history of the development of neural networks and their impact on business
- Discusses the Perceptron neural model and its limitations
- Includes backpropagation, the most commonly used learning paradigm for business neural networks
- Includes case studies showing backpropagation learning applied to classification and prediction problems
- Shows how self-organisation is particularly suited to clustering of large data sets
- Covers adaptive resonance theory
- Shows backpropagation and self-organisation in the context of data mining
- The purposes of data mining are outlined
- A methodology for mining hidden information from large data sets is presented
- Covers the concepts of directed and undirected knowledge discovery
- Includes data mining case studies from the insurance and telecommunications industries
- Introduces intelligent techniques like genetic algorithms, fuzzy logic, expert systems, and hybridisations
- Speculates about the future of neural network research and business applications
Buy this book now with your Credit Card:
Introduction to Neural Networks and Data Mining
Eruditions Publishing, Melbourne, 1998.
Papercover, 160 pages, 175 mm x 245 mm.
ISBN 978-186491-004-9 RRP AU$ 59.95
[Postage/Handling: Within Australia AU$15; International, AU$25 Air Mail]
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