Principles of Data Mining

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Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Bramer, Max (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Springer London : Imprint: Springer, 2013.
Edición:2nd ed. 2013.
Colección:Undergraduate Topics in Computer Science,
Temas:
Information storage and retrieval systems.
Database management.
Artificial intelligence.
Computer programming.
Information Storage and Retrieval.
Database Management.
Artificial Intelligence.
Programming Techniques.
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
  • 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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