TY  - BOOK
AU  - Bramer,M.A.
TI  - Principles of data mining
T2  - Undergraduate topics in computer science,
SN  - 9781447174936
U1  - 006.312 22
PY  - 2020///
CY  - London
PB  - Springer
KW  - Computer science
KW  - Data mining
KW  - fast
N1  - 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
UR  - http://www.vlebooks.com/vleweb/product/openreader?id=WestLondon&isbn=9781447174936
ER  -