Big Data Mining

10.1057/9781137379283 - Predictive Analytics, Data Mining and Big Data, Steven Finlay

Copyright material from www.palgraveconnect.com - licensed to npg - PalgraveConnect - 2016-02-17

Predictive Analytics, Data Mining and Big Data


This page intentionally left blank


Myths, Misconceptions and Methods

Steven Finlay



Predictive Analytics, Data Mining and Big Data

© Steven Finlay 2014 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No portion of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6–10 Kirby Street, London EC1N 8TS.

The author has asserted his right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2014 by PALGRAVE MACMILLAN Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS. Palgrave Macmillan in the US is a division of St Martin’s Press LLC, 175 Fifth Avenue, New York, NY 10010. Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world. Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries. ISBN 978–1–137–37927–6 This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. Logging, pulping and manufacturing processes are expected to conform to the environmental regulations of the country of origin. A catalogue record for this book is available from the British Library. A catalog record for this book is available from the Library of Congress. Typeset by MPS Limited, Chennai, India.



Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages.


To Ruby and Samantha



This page intentionally left blank


Figures and Tables

x

Acknowledgments

xii

1

Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

1

What are data mining and predictive analytics? 2 How good are models at predicting behavior? 6 What are the benefits of predictive models? 7 Applications of predictive analytics 9 Reaping the benefits, avoiding the pitfalls 11 What is Big Data? 13 How much value does Big Data add? 16 The rest of the book 19

2 Using Predictive Models 2.1 2.2 2.3 2.4 2.5

21

What are your objectives? 22 Decision making 23 The next challenge 31 Discussion 34 Override rules (business rules) 36

Analytics, Organization and Culture 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

39

Embedded analytics 40 Learning from failure 42 A lack of motivation 43 A slight misunderstanding 45 Predictive, but not precise 50 Great expectations 52 Understanding cultural resistance to predictive analytics The impact of predictive analytics 60

54

vi i

3


Contents


3.9

4

Combining model-based predictions and human judgment 62

The Value of Data

65

4.1 What type of data is predictive of behavior? 66 4.2 Added value is what’s important 70 4.3 Where does the data to build predictive models come from? 73 4.4 The right data at the right time 76 4.5 How much data do I need to build a predictive model?

5

Ethics and Legislation 5.1 5.2 5.3 5.4 5.5 5.6 5.7

6

85

A brief introduction to ethics 86 Ethics in practice 89 The relevance of ethics in a Big Data world Privacy and data ownership 92 Data security 96 Anonymity 97 Decision making 99

Types of Predictive Models

90

104

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9

Linear models 106 Decision trees (classification and regression trees) 112 (Artificial) neural networks 114 Support vector machines (SVMs) 118 Clustering 120 Expert systems (knowledge-based systems) 122 What type of model is best? 124 Ensemble (fusion or combination) systems 128 How much benefit can I expect to get from using an ensemble? 130 6.10 The prospects for better types of predictive models in the future 131

7

The Predictive Analytics Process 7.1 7.2 7.3 7.4 7.5

134

Project initiation 135 Project requirements 138 Is predictive analytics the right tool for the job? Model building and business evaluation 143 Implementation 145

142


79


vi ii

Contents

7.6 Monitoring and redevelopment 149 7.7 How long should a predictive analytics project take?

8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11

9

10

157

Exploring the data landscape 158 Sampling and shaping the development sample 162 Data preparation (data cleaning) Creating derived data 163 Understanding the data 164 Preliminary variable selection (data reduction) Pre-processing (data transformation) 166 Model construction (modeling) 170 Validation 171 Selling models into the business 172 The rise of the regulator 176

Text Mining and Social Network Analysis 9.1 9.2 9.3 9.4 9.5 9.6 9.7

154

165

179

Text mining 179 Using text analytics to create predictor variables Within document predictors 181 Sentiment analysis 184 Across document predictors 185 Social network analysis 186 Mapping a social network 191

Hardware, Software and All that Jazz

159

181

194

10.1 Relational databases 197 10.2 Hadoop 200 10.3 The limitations of Hadoop 202 10.4 Do I need a Big Data solution to do predictive analytics? 203 10.5 Soft ware for predictive analytics 206 Appendix A. Glossary of Terms

209

Appendix B. Further Sources of Information Appendix C. Lift Charts and Gain Charts Notes

227

Index

246

218 223



8 How to Build a Predictive Model

ix

Contents

You have reached the end of the preview for this book / chapter. You are viewing this book in preview mode, which allows selected pages to be viewed without a current Palgrave Connect subscription. Pages beyond this point are only available to subscribing institutions. If you would like access the full book for your institution please: Contact your librarian directly in order to request access, or; Use our Library Recommendation Form to recommend this book to your library (http://www.palgraveconnect.com/pc/connect/info/recommend.html), or; Use the 'Purchase' button above to buy a copy of the title from http://www.palgrave.com or an approved 3rd party. If you believe you should have subscriber access to the full book please check you are accessing Palgrave Connect from within your institution's network, or you may need to login via our Institution / Athens Login page: (http://www.palgraveconnect.com/pc/nams/svc/institutelogin? target=/index.html).

Please respect intellectual property rights This material is copyright and its use is restricted by our standard site license terms and conditions (see http://www.palgraveconnect.com/pc/connect/info/terms_conditions.html). If you plan to copy, distribute or share in any format including, for the avoidance of doubt, posting on websites, you need the express prior permission of Palgrave Macmillan. To request permission please contact [email protected].

preview.html[22/12/2014 16:51:21]

Big Data Mining

Recommend Documents