Principles of Data Mining / Edition 1 available in Paperback
- Pub. Date:
- Springer-Verlag New York, LLC
This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.
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.