Principles of Data Mining 1st edition by Max Bramer – Ebook PDF Instant Download/Delivery: 1447174925 , 978-1447174929
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ISBN 10: 1447174925
ISBN 13: 978-1447174929
Author: Max Bramer
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 clustering.
Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail.
It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the ‘black box’ so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field.
Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.
Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time – a phenomenon known as concept drift.
The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.
Principles of Data Mining 1st Table of contents:
1. Introduction to Data Mining
2. Data for Data Mining
3. Introduction to Classification: Naïve Bayes and Nearest Neighbour
4. Using Decision Trees for Classification
5. Decision Tree Induction: Using Entropy for Attribute Selection
6. Decision Tree Induction: Using Frequency Tables for Attribute Selection
7. Estimating the Predictive Accuracy of a Classifier
8. Continuous Attributes
9. Avoiding Overfitting of Decision Trees
10. More About Entropy
11. Inducing Modular Rules for Classification
12. Measuring the Performance of a Classifier
13. Dealing with Large Volumes of Data
14. Ensemble Classification
15. Comparing Classifiers
16. Association Rule Mining I
17. Association Rule Mining II
18. Association Rule Mining III: Frequent Pattern Trees
19. Clustering
20. Text Mining
21. Classifying Streaming Data
22. Classifying Streaming Data II: Time-Dependent Data
23. An Introduction to Neural Networks
Back Matter
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