Computational methods for data analysis:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
De Gruyter
[2019]
|
| Schriftenreihe: | De Gruyter STEM
|
| Schlagworte: | |
| Online-Zugang: | http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110496352&searchTitles=true https://www.degruyter.com/doc/cover/9783110496352.jpg Inhaltsverzeichnis |
| Beschreibung: | XII, 383 Seiten Illustrationen, Diagramme |
| ISBN: | 9783110496352 3110496356 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804178486028402688 |
|---|---|
| adam_text | Contents
Preface--V
Acknowledgment--VII
1
1.1
1.2
1.3
1.4
1.5
1.5.1
2
2.1
2.2
2.2.1
2.3
2.3.1
2.3.2
2.4
2.4.1
2.4.2
2.4.3
2.5
2.5.1
2.5.2
2.5.3
2.5.4
2.6
3
3.1
3.2
3.2.1
3.2.2
3.2.3
3.3
Introduction----1
Objectives-----3
Intended audience-----3
Features of the chapters----4
Content of the chapters-----5
Text supplements------7
Datasets----7
Dataset----9
Big data and data science-----12
Data objects, attributes and types of attributes--15
Nominal attributes and numeric attributes-----18
Basic definitions of real and synthetic data--19
Real dataset----20
Synthetic dataset-----20
Real and synthetic data from the fields of economy and
medicine----26
Economy data: Economy (U.N.I.S.) dataset------26
MS data and content (neurology and radiology data): MS
dataset-----26
Clinical psychology data: WAIS-R dataset------39
Basic statistical descriptions of data--54
Central tendency: Mean, median and mode-------55
Spread of data-----57
Measures of data dispersion------60
Graphic displays------61
Data matrix versus dissimilarity matrix---65
References-----67
Data preprocessing and model evaluation-----71
Data quality----71
Data preprocessing: Major steps involved------72
Data cleaning and methods-----73
Data cleaning as a process----74
Data integration and methods-----75
Data value conflict detection and resolution--81
X
Contents
3.4
3.5
3.6
3.7
3.8
3.8.1
3.9
3.9.1
4
4.1
4.1.1
4.1.2
4.1.3
4.2
4.2.1
4.2.2
4.3
4.3.1
4.3.2
5
5.1
5.1.1
5.1.2
5.1.3
5.2
5.2.1
5.2.2
5.2.3
6
6.1
6.2
Data smoothing and methods-------81
Data reduction----83
Data transformation------84
Attribute subset selection----84
Classification of data---88
Definition of classification--89
Model evaluation and selection-----91
Metrics for evaluating classifier performance---92
References-----101
Algorithms------103
What is an algorithm?----104
What is the flowchart of an algorithm?----104
Fundamental concepts of programming-------105
Expressions and repetition statements-----108
Image on coordinate systems------112
Pixel coordinates----112
Color models------113
Statistical application with algorithms: Image on coordinate
systems-----114
Statistical application with algorithms: BW image on coordinate
systems-----116
Statistical application with algorithms: RGB image on coordinate
systems-----130
References-----144
Linear model and multilinear model-----147
Linear model analysis for various data----156
Application of economy dataset based on linear model-----156
Linear model for the analysis of MS----159
Linear model for the analysis of mental functions---162
Multilinear model algorithms for the analysis of various data---165
Multilinear model for the analysis of economy (U.N.I.S.)
dataset-----165
Multilinear model for the analysis of MS--166
Multilinear model for the analysis of mental functions---169
References-----171
Decision Tree---173
Decision tree induction----174
Attribute selection measures-----178
Contents
XI
6.3
6.3.1
6.4
6.4.1
6.5
6.5.1
7
7.1
7.1.1
7.1.2
7.1.3
8
8.1
8.2
8.3
8.3.1
8.3.2
8.3.3
9
9.1
9.1.1
9.1.2
9.1.3
10
10.1
10.1.1
10.2
10.2.1
10.3
10.3.1
Iterative dichotomiser 3 (ID3) algorithm--178
ID3 algorithm for the analysis of various data---185
C4.5 algorithm----195
C4.5 Algorithm for the analysis of various data--199
CART algorithm----208
CART algorithm for the analysis of various data--217
References----226
Naive Bayesian classifier----229
Naive Bayesian classifier algorithm (and its types) for the analysis
of various data---237
Naive Bayesian classifier algorithm for the analysis of economy
(U.N.I.S.)---237
Algorithms for the analysis of multiple sclerosis--241
Naive Bayesian classifier algorithm for the analysis of mental
functions----246
References----250
Support vector machines algorithms------251
The case with data being linearly separable---251
The case when the data are linearly inseparable----257
SVM algorithm for the analysis of various data---260
SVM algorithm for the analysis of economy (U.N.I.S.) Data---260
SVM algorithm for the analysis of multiple sclerosis--263
SVM algorithm for the analysis of mental functions----267
References----270
/(-Nearest neighbor algorithm----273
/(-Nearest algorithm for the analysis of various data-277
/(-Nearest algorithm for the analysis of Economy (U.N.I.S.)-277
/(-Nearest algorithm for the analysis of multiple sclerosis-280
/(-Nearest algorithm for the analysis of mental functions---284
References----287
Artificial neural networks algorithm----289
Classification by backpropagation----289
A multilayer feed-forward neural network----290
Feed-forward backpropagation (FFBP) algorithm------291
FFBP algorithm for the analysis of various data--297
LVQ algorithm-----311
LVQ algorithm for the analysis of various data---313
References----321
XII
Contents
11 Fractal and multifractal methods with ANN----------323
11.1 Basic descriptions of fractal----323
11.2 Fractal dimension----334
11.3 Multifractal methods-----335
11.3.1 Two-dimensional fractional Brownian motion------335
11.3.2 Holder regularity----337
11.3.3 Fractional Brownian motion-------338
11.4 Multifractal analysis with LVQ algorithm----352
11.4.1 Polynomial Holder function with LVQ algorithm for the analysis of
various data----355
11.4.2 Exponential Holder function with LVQ algorithm for the analysis of
various data----363
References------370
Index
375
|
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| ctrlnum | (OCoLC)1083271652 (DE-599)DNB1152828576 |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
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| illustrated | Illustrated |
| indexdate | 2024-07-10T08:04:44Z |
| institution | BVB |
| institution_GND | (DE-588)10095502-2 |
| isbn | 9783110496352 3110496356 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030311916 |
| oclc_num | 1083271652 |
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| owner_facet | DE-20 DE-355 DE-BY-UBR DE-384 DE-739 DE-522 DE-19 DE-BY-UBM DE-578 DE-703 DE-634 DE-83 DE-11 |
| physical | XII, 383 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | De Gruyter |
| record_format | marc |
| series2 | De Gruyter STEM |
| spelling | Karaca, Yeliz Verfasser (DE-588)1174000813 aut Computational methods for data analysis Yeliz Karaca, Carlo Cattani Berlin De Gruyter [2019] © 2019 XII, 383 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier De Gruyter STEM Datenanalyse (DE-588)4123037-1 gnd rswk-swf Numerisches Verfahren (DE-588)4128130-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Fachhochschul-/Hochschulausbildung Bioinformatik Datenanalyse Maschinelles Lernen Massendaten Ökonometrie Datenanalyse (DE-588)4123037-1 s Numerisches Verfahren (DE-588)4128130-5 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Cattani, Carlo 1954- Verfasser (DE-588)1076126375 aut Walter de Gruyter GmbH & Co. KG (DE-588)10095502-2 pbl Erscheint auch als Online-Ausgabe, PDF 978-3-11-049636-9 Erscheint auch als Online-Ausgabe, EPUB 978-3-11-049360-3 X:MVB http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110496352&searchTitles=true X:MVB https://www.degruyter.com/doc/cover/9783110496352.jpg Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030311916&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Karaca, Yeliz Cattani, Carlo 1954- Computational methods for data analysis Datenanalyse (DE-588)4123037-1 gnd Numerisches Verfahren (DE-588)4128130-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4128130-5 (DE-588)4193754-5 |
| title | Computational methods for data analysis |
| title_auth | Computational methods for data analysis |
| title_exact_search | Computational methods for data analysis |
| title_full | Computational methods for data analysis Yeliz Karaca, Carlo Cattani |
| title_fullStr | Computational methods for data analysis Yeliz Karaca, Carlo Cattani |
| title_full_unstemmed | Computational methods for data analysis Yeliz Karaca, Carlo Cattani |
| title_short | Computational methods for data analysis |
| title_sort | computational methods for data analysis |
| topic | Datenanalyse (DE-588)4123037-1 gnd Numerisches Verfahren (DE-588)4128130-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Datenanalyse Numerisches Verfahren Maschinelles Lernen |
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| work_keys_str_mv | AT karacayeliz computationalmethodsfordataanalysis AT cattanicarlo computationalmethodsfordataanalysis AT walterdegruytergmbhcokg computationalmethodsfordataanalysis |