Principles of Data Mining

Principles of Data Mining

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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...

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Bibliographic Details
Main Author: Bramer, Max (Author, http://id.loc.gov/vocabulary/relators/aut)
Format: Electronic Book
Language:English
Published: London : Springer London : Imprint: Springer, 2020
Edition:4th ed. 2020
Series:Undergraduate Topics in Computer Science,
Subjects:
Artificial intelligence
Computer programming
Database management
Information storage and retrieval
Information Storage and Retrieval
Artificial Intelligence
Database Management
Programming Techniques
Table of Contents:
  • 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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