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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Principles of Data Mining

Principles of Data Mining

Bramer, Max

Paperback

Series: Undergraduate Topics in Computer Science

DatabasesGeneral ComputersSystem Administration

ISBN10: 1447174925
ISBN13: 9781447174929
Publisher: Springer Nature
Published: May 21 2020
Pages: 571
Weight: 1.79
Height: 1.19 Width: 6.14 Depth: 9.21
Language: English

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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