Principles of Data Mining / by Max Bramer.

Format:

Book

Author/Creator:

Bramer, M. A. (Max A.), 1948- author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Undergraduate topics in computer science 1863-7310

Undergraduate Topics in Computer Science, 1863-7310

Language:

English

Subjects (All):

Information storage and retrieval.

Database management.

Artificial intelligence.

Computer programming.

Information Storage and Retrieval.

Database Management.

Artificial Intelligence.

Programming Techniques.

Local Subjects:

Information Storage and Retrieval.

Database Management.

Artificial Intelligence.

Programming Techniques.

Physical Description:

1 online resource (XVI, 571 pages) : 138 illustrations.

Edition:

Fourth edition 2020.

Contained In:

Springer Nature eBook

Place of Publication:

London : Springer London : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-1-4471-7493-6

9781447174936

Access Restriction:

Restricted for use by site license.