ISBN-10:
1846287650
ISBN-13:
9781846287657
Pub. Date:
03/28/2007
Publisher:
Springer-Verlag New York, LLC
Principles of Data Mining / Edition 1

Principles of Data Mining / Edition 1

by Max Bramer

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Product Details

ISBN-13: 9781846287657
Publisher: Springer-Verlag New York, LLC
Publication date: 03/28/2007
Series: Undergraduate Topics in Computer Science
Edition description: 1st Edition.
Pages: 354
Product dimensions: 7.00(w) x 9.20(h) x 0.70(d)

About the Author

Max Bramer is Emeritus Professor of Information Technology at the University of Portsmouth, England, Vice-President of the International Federation for Information Processing (IFIP) and Chair of the British Computer Society Specialist Group on Artificial Intelligence.


He has been actively involved since the 1980s in the field that has since come to be called by names such as Data Mining, Knowledge Discovery in Databases, Big Data and Predictive Analytics. He has carried out many projects in the field, particularly in relation to automatic classification of data, and has published extensively in the technical literature. He has taught the subject to both undergraduate and postgraduate students for many years.


Some of Max Bramer’s other Springer publications include:


Research and Development in Intelligent Systems


Artificial Intelligence in Theory and Practice


Artificial Intelligence: an International Perspective



Logic Programming with Prolog


Web Programming with PHP and MySQL

Table of Contents

Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naive Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More about Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Association Rule Mining I.- Association Rule Mining II.- Clustering.- Text Mining.- References.- Appendix A: Essential Mathematics.- Appendix B: Datasets.- Appendix C: Sources of Further Information.- Appendix D: Glossary and Notation.- Appendix E: Solutions to Self-assessment Exercises.- Index.

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