Principles of data mining / Max Bramer.
Material type: TextLanguage: English Series: Undergraduate topics in computer sciencePublication details: London : Springer, 2020.Edition: 4th edDescription: xvi, 571 p. ; ill. pbkISBN:- 9781447174929
- 9781447174936
- 1447174933
- 006.312 22 BRA
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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General Books | CUTN Central Library Philosophy & psychology | Non-fiction | 006.312 BRA (Browse shelf(Opens below)) | Available | 46859 |
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Academic
Includes bibliographical references and index.
Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve 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.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- 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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