Data analysis and data mining: an introduction
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford [u.a.]
Oxford Univ. Press
2012
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | VIII, 278 S. graph. Darst. |
| ISBN: | 9780199767106 |
Staff View
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Record in the Search Index
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CONTENTS
Preface vii
Preface to the English Edition
ix
1.
Introduction
1
1.1.
New problems and new opportunities
1
1.2.
All models are wrong
9
1.3.
A matter of style
12
2.
A-B-C
15
2.1.
Old friends: Linear models
15
2.2.
Computational aspects
30
2.3.
Likelihood
33
2.4.
Logistic regression and GLM
40
Exercises
44
3.
Optimism, Conflicts, and Trade-offs
45
3.1.
Matching the conceptual frame and real life
45
3.2.
A simple prototype problem
46
3.3.
Ifweknew/(.v).
47
3.4.
But as we do not know
ƒ
(x).
. 51
3.5.
Methods for model selection
52
3.6.
Reduction of dimensions and selection of most
appropriate model
58
Exercises
66
4.
Prediction of Quantitative Variables
68
4.1.
Nonparametric estimation: Why?
68
4.2.
Local regression
69
4.3.
The curse of dimensionality
78
4.4.
Splines
79
4.5.
Additive models and GAM
89
4.6.
Projection pursuit
93
4.7.
Inferential aspects
94
4.8.
Regression trees
98
4.9.
Neural networks
106
4.10.
Case studies 111
Exercises
132
CONTENTS
5.
Methods of Classification
134
5.1.
Prediction of categorical variables
134
5.2.
An introduction based on a marketing problem
135
5.3.
Extension to several categories
142
5.4.
Classification via linear regression
149
5.5.
Discriminant analysis
154
5.6.
Some nonparametric methods
159
5.7.
Classification trees
164
5.8.
Some other topics
168
5.9.
Combination of classifiers
176
5.10.
Case studies
183
Exercises
210
6.
Methods of Internal Analysis
212
6.1.
Cluster analysis
212
6.2.
Associations among variables
222
6.3.
Case study: Web usage mining
232
Appendix A Complements of Mathematics and Statistics
240
А.1.
Concepts on linear algebra
240
A.2. Concepts of probability theory
241
A3.
Concepts of linear models
246
Appendix
В
Data Sets
254
B.I. Simulated data
254
B.2.
Cardata
254
B.3. Brazilian bank data
255
B.4. Data for telephone company customers
256
B.5. Insurance data
257
B.6. Choice of fruit juice data
258
B.7. Customer satisfaction
259
B.8. Web usage data
261
Appendix
С
Symbols and Acronyms
263
References
265
Author Index
269
Subject Index
271
"^ n
introduction to statistical data mining, Data Analysis and Data Mining
і
is both a textbook and a professional resource. Assuming only a basic
knowledge of statistical reasoning, it presents core concepts in data mining and
ixploratory statistical models to students and professional statisticians—both
those working in business and those working in a technological or scientific
capacity—who have a limited knowledge of data mining.
This book presents key statistical concepts by way of case studies, giv¬
ing readers the benefit of learning from real problems and real data. Aided by
a diverse range of statistical methods and techniques, readers will move from
simple problems to complex problems. Through these case studies, authors
Adelchi Azzalini and Bruno
Scarpa
explain the rationale and the working of sta¬
tistical methods, rather than relying on the "push the button" philosophy. They
demonstrate how to use statistical tools to find the best solution to a given
problem.
Case studies feature current topics highly relevant to data mining, such
as web page traffic; the segmentation of customers: selection of customers for
direct mail commercial campaigns: and measurements of customer satisfac¬
tion. Appropriate for both advanced undergraduate and graduate students, this
much-needed book will fill a gap between higher level books, which emphasize
technical explanations, and lower level books, which assume no prior knowl¬
edge and do not explain the methodology behind the statistical operations.
Adelchi Azzalini is Professor of Statistics in the Department of Statistical
Sciences at the University of Padua. He actively researches in statistical meth¬
odology and has maintained an interest in statistical applications.
Bruno
Scarpa
is Researcher in the Department of Statistical Sciences at the
University of Padua. He previously worked as a statistician and data miner in
marketing divisions of insurance and telecommunication companies.
Cover design: Eve
Siegel
Cover image: Created by Adelchi Azzalini
&
Bruno
Scarpa
ISBN
978-0-19-976710-6
90000
OXTORD
www.oup.com |
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| spelling | Azzalini, Adelchi Verfasser aut Analisi dei dati e "data mining" Data analysis and data mining an introduction Adelchi Azzalini and Bruno Scarpa Oxford [u.a.] Oxford Univ. Press 2012 VIII, 278 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Datenanalyse (DE-588)4123037-1 s DE-604 Scarpa, Bruno Verfasser aut Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024808607&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024808607&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
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| subject_GND | (DE-588)4123037-1 (DE-588)4428654-5 |
| title | Data analysis and data mining an introduction |
| title_alt | Analisi dei dati e "data mining" |
| title_auth | Data analysis and data mining an introduction |
| title_exact_search | Data analysis and data mining an introduction |
| title_full | Data analysis and data mining an introduction Adelchi Azzalini and Bruno Scarpa |
| title_fullStr | Data analysis and data mining an introduction Adelchi Azzalini and Bruno Scarpa |
| title_full_unstemmed | Data analysis and data mining an introduction Adelchi Azzalini and Bruno Scarpa |
| title_short | Data analysis and data mining |
| title_sort | data analysis and data mining an introduction |
| title_sub | an introduction |
| topic | Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Datenanalyse Data Mining |
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