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Principles of Data Mining
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clus...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Springer London : Imprint: Springer,
2016.
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| Edición: | 3rd ed. 2016. |
| Colección: | Undergraduate Topics in Computer Science,
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| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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
- 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.