Data Mining Algorithms E-Book

Pawel Cichosz

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1177

Veröffentlichungsjahr: 2014

Beschreibung

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

Leseprobe

Table of Contents

Title Page

Copyright

Dedication

Acknowledgements

Preface

Data mining

Motivation

Organization

Notation

R code examples

Website

Further readings

References

Part I: Preliminaries

Chapter 1: Tasks

1.1 Introduction

1.2 Inductive learning tasks

1.3 Classification

1.4 Regression

1.5 Clustering

1.6 Practical issues

1.7 Conclusion

1.8 Further readings

References

Chapter 2: Basic statistics

2.1 Introduction

2.2 Notational conventions

2.3 Basic statistics as modeling

2.4 Distribution description

2.5 Relationship detection

2.6 Visualization

2.7 Conclusion

2.8 Further readings

References

Part II: Classification

Chapter 3: Decision trees

3.1 Introduction

3.2 Decision tree model

3.3 Growing

3.4 Pruning

3.5 Prediction

3.6 Weighted instances

3.7 Missing value handling

3.8 Conclusion

3.9 Further readings

References

Chapter 4: Naïve Bayes classifier

4.1 Introduction

4.2 Bayes rule

4.3 Classification by Bayesian inference

4.4 Practical issues

4.5 Conclusion

4.6 Further readings

References

Chapter 5: Linear classification

5.1 Introduction

5.2 Linear representation

5.3 Parameter estimation

5.4 Discrete attributes

5.5 Conclusion

5.6 Further readings

References

Chapter 6: Misclassification costs

6.1 Introduction

6.2 Cost representation

6.3 Incorporating misclassification costs

6.4 Effects of cost incorporation

6.5 Experimental procedure

6.6 Conclusion

6.7 Further readings

References

Chapter 7: Classification model evaluation

7.1 Introduction

7.2 Performance measures

7.3 Evaluation procedures

7.4 Conclusion

7.5 Further readings

References

Part III: Regression

Chapter 8: Linear regression

8.1 Introduction

8.2 Linear representation

8.3 Parameter estimation

8.4 Discrete attributes

8.5 Advantages of linear models

8.6 Beyond linearity

8.7 Conclusion

8.8 Further readings

References

Chapter 9: Regression trees

9.1 Introduction

9.2 Regression tree model

9.3 Growing

9.4 Pruning

9.5 Prediction

9.6 Weighted instances

9.7 Missing value handling

9.8 Piecewise linear regression

9.9 Conclusion

9.10 Further readings

References

Chapter 10: Regression model evaluation

10.1 Introduction

10.2 Performance measures

10.3 Evaluation procedures

10.4 Conclusion

10.5 Further readings

References

Part IV: Clustering

Chapter 11: (Dis)similarity measures

11.1 Introduction

11.2 Measuring dissimilarity and similarity

11.3 Difference-based dissimilarity

11.4 Correlation-based similarity

11.5 Missing attribute values

11.6 Conclusion

11.7 Further readings

References

Chapter 12: k-Centers clustering

12.1 Introduction

12.2 Algorithm scheme

12.3

k

-Means

12.4 Beyond means

12.5 Beyond (fixed)

k

12.6 Explicit cluster modeling

12.7 Conclusion

12.8 Further readings

References

Chapter 13: Hierarchical clustering

13.1 Introduction

13.2 Cluster hierarchies

13.3 Agglomerative clustering

13.4 Divisive clustering

13.5 Hierarchical clustering visualization

13.6 Hierarchical clustering prediction

13.7 Conclusion

13.8 Further readings

References

Chapter 14: Clustering model evaluation

14.1 Introduction

14.2 Per-cluster quality measures

14.3 Overall quality measures

14.4 External quality measures

14.5 Using quality measures

14.6 Conclusion

14.7 Further readings

References

Part V: Getting Better Models

Chapter 15: Model ensembles

15.1 Introduction

15.2 Model committees

15.3 Base models

15.4 Model aggregation

15.5 Specific ensemble modeling algorithms

15.6 Quality of ensemble predictions

15.7 Conclusion

15.8 Further readings

References

Chapter 16: Kernel methods

16.1 Introduction

16.2 Support vector machines

16.3 Support vector regression

16.4 Kernel trick

16.5 Kernel functions

16.6 Kernel prediction

16.7 Kernel-based algorithms

16.8 Conclusion

16.9 Further readings

References

Chapter 17: Attribute transformation

17.1 Introduction

17.2 Attribute transformation task

17.3 Simple transformations

17.4 Multiclass encoding

17.5 Conclusion

17.6 Further readings

References

Chapter 18: Discretization

18.1 Introduction

18.2 Discretization task

18.3 Unsupervised discretization

18.4 Supervised discretization

18.5 Effects of discretization

18.6 Conclusion

18.7 Further readings

References

Chapter 19: Attribute selection

19.1 Introduction

19.2 Attribute selection task

19.3 Attribute subset search

19.4 Attribute selection filters

19.5 Attribute selection wrappers

19.6 Effects of attribute selection

19.7 Conclusion

19.8 Further readings

References

Chapter 20: Case studies

20.1 Introduction

20.2 Census income

20.3 Communities and crime

20.4 Cover type

20.5 Conclusion

20.6 Further readings

References

Closing

Retrospecting

Final words

A: Notation

A.1 Attribute values

A.2 Data subsets

A.3 Probabilities

B: R packages

B.1 CRAN packages

B.2 DMR packages

B.3 Installing packages

References

C: Datasets

Index

End User License Agreement

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Guide

Cover

Table of Contents

preface

Part I: Preliminaries

Begin Reading

List of Illustrations

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 3.1

Figure 3.2

Figure 5.1

Figure 5.2

Figure 5.3

Figure 5.4

Figure 6.1

Figure 6.2

Figure 6.3

Figure 7.1

Figure 7.2

Figure 7.3

Figure 7.4

Figure 7.5

Figure 7.6

Figure 7.7

Figure 7.8

Figure 7.9

Figure 7.10

Figure 9.1

Figure 10.1

Figure 10.2

Figure 12.1

Figure 12.2

Figure 12.3

Figure 12.4

Figure 13.1

Figure 13.2

Figure 14.1

Figure 14.2

Figure 15.1

Figure 15.2

Figure 15.3

Figure 15.4

Figure 15.5

Figure 15.6

Figure 15.7

Figure 15.8

Figure 16.1

Figure 16.2

Figure 16.3

Figure 16.4

Figure 16.5

Figure 16.6

Figure 17.1

Figure 18.1

Figure 18.2

Figure 19.1

Figure 19.2

Figure 19.3

Figure 19.4

Figure 19.5

Figure 19.6

Figure 20.1

Figure 20.2

Figure 20.3

Figure 20.4

Figure 20.5

Figure 20.6

Figure 20.7

Figure 20.8

Figure 20.9

Figure 20.10

Figure 20.11

Figure 20.12

Figure 20.13

Figure 20.14

Figure 20.15

Figure 20.16

Figure 20.17

Figure 20.18

Figure 20.19

Figure 20.20

Figure 20.21

Figure 20.22

Figure 20.23

List of Tables

Table 6.1

Table 7.1

Table 7.2

Table 7.3

Table 7.4

Table 7.5

Table 17.1

Data Mining Algorithms: Explained Using R

Paweł Cichosz

Department of Electronics and Information TechnologyWarsaw University of TechnologyPoland

This edition first published 2015

© 2015 by John Wiley & Sons, Ltd

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Library of Congress Cataloging-in-Publication Data

Cichosz, Pawel, author.

Data mining algorithms : explained using R / Pawel Cichosz.

pages cm

Summary: “This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective” –Provided by publisher.

Includes bibliographical references and index.

ISBN 978-1-118-33258-0 (hardback)

1. Data mining. 2. Computer algorithms. 3. R (Computer program language) I. Title.

QA76.9.D343C472 2015

006.3′12–dc23

2014036992

A catalogue record for this book is available from the British Library.

ISBN: 9781118332580

To my wife, Joanna, and my sons, Grzegorz and Łukasz

Acknowledgements

With the rise and rapidly growing popularity of online idea sharing methods, such as blogs and wikis, traditional books are no longer the only way of making large portions of text available to a wide audience. The former are particularly suitable for collaborative or social writing and readings undertakings, often with mixed reader–writer roles of particular participants. For individual writing and reading efforts the traditional book form (although not necessarily tied to the paper media) still remains the best approach. On one hand, it clearly assigns full and exclusive responsibility for the contents to the author, with no easy excuses for errors and other deficiencies. On the other hand, there are several other people engaged in the publishing process who help to give the book its final shape and protect the audience against a totally flawed work.

As the author of this book, I feel indeed totally responsible for all its imperfections, only some which I am aware of, but I have no doubts that there are many more of them. With that being said, several people from the editorial and production team worked hard to make the imperfect outcome of my work worth publishing. My thanks go, in particular, to Richard Davies, Prachi Sinha Sahay, Debbie Jupe, and Kay Heather from Wiley for their encouragement, support, understanding, and reassuring professionalism at all stages of writing and production. Radhika Sivalingam, Lincy Priya, and Yogesh Kukshal did their best to transform my manuscript into a real book, meeting publication standards. I believe there are others who contributed to this book's production that I am not even aware of and I am grateful to them all, also.

I was thoughtless enough to share my intention to write this book with my colleagues from the Artificial Intelligence Applications Research Group at the Warsaw University of Technology. While their warm reception of this idea and constant words of encouragement were extremely helpful, I wished I had not done that many times. It would have been so much easier to give up if I had kept this in secret. Perhaps the ultimate reason why I continued to work despite hesitations is that I knew they would keep asking and I would be unable to find a good excuse. Several thoughts expressed in this book were shaped by discussions during our group's seminar meetings. Interacting with my colleagues from the analytics teams at Netezza Poland, IBM Poland, and iQor Poland, with which I had an opportunity to work on some data mining projects at different stages of writing the book, was also extremely stimulating, although the contents of the book have no relationships with the projects I was involved with.

I owe special thanks to my wife and two sons, who did not directly contribute to the contents of this book, but made it possible by allowing me to spend much of my time that should be normally devoted to them on this work and providing constant encouragement. If you guys can read these thanks in a published copy of the book, then it means it is all over at last and we will hopefully get back to normal life.

Preface

Data mining

Data mining has been a rapidly growing field of research and practical applications during the last two decades. From a somewhat niche academic area at the intersection of machine learning and statistics it has developed into an established scientific discipline and a highly valued branch of the computing industry. This is reflected by data mining becoming an essential part of computer science education as well as the increasing overall awareness of the term “data mining” among the general (not just computing-related) academic and business audience.

Scope

Various definitions of data mining may be found in the literature. Some of them are broad enough to include all types of data analysis, regardless of the representation and applicability of their results. This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective. According to this perspective the ultimate goal of data mining is delivering predictive models. The latter can be thought of as computationally represented chunks of knowledge about some domain of interest, described by the analyzed data, that are capable of providing answers to queries transcending the data, i.e., such that cannot be answered by just extracting and aggregating values from the data. Such knowledge is discovered from data by capturing and generalizing useful relationship patterns that occur therein.

Activities needed for creating predictive models based on data and making sure that they meet the application's requirements fall in the scope of data mining as understood in this book. Analytical activities which do not contribute to model creation—although they may still deliver extremely useful results—remain therefore beyond the scope of our interest. This still leaves a lot of potential contents to be covered, including not only modeling algorithms, but also techniques for evaluating the quality of predictive models, transforming data to make modeling algorithms easier to apply or more likely to succeed, selecting attributes most useful for model creation, and combining multiple models for better predictions.