Data analytics: models and algorithms for intelligent data analysis
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Wiesbaden
Springer Vieweg
2020
|
| Ausgabe: | Third edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Beschreibung: | xii, 161 Seiten Diagramme |
| ISBN: | 9783658297787 |
Internformat
MARC
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|---|---|---|---|
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| 245 | 1 | 0 | |a Data analytics |b models and algorithms for intelligent data analysis |c Thomas A. Runkler |
| 250 | |a Third edition | ||
| 264 | 1 | |a Wiesbaden |b Springer Vieweg |c 2020 | |
| 300 | |a xii, 161 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
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| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 653 | |a classification | ||
| 653 | |a deep learning | ||
| 653 | |a business intelligence | ||
| 653 | |a clustering | ||
| 653 | |a algorithms | ||
| 653 | |a forecasting | ||
| 653 | |a data mining | ||
| 653 | |a knowledge discovery | ||
| 653 | |a machine learning | ||
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Datensatz im Suchindex
| _version_ | 1804181617856479232 |
|---|---|
| adam_text | CONTENTS
1
INTRODUCTION
....................................................................................
1
1.1
IT
*
S
ALL
ABOUT
DATA
.........................................................................................
1
1.2
DATA
ANALYTICS,
DATA MINING,
AND
KNOWLEDGE
DISCOVERY
............................
2
REFERENCES
................................................................................................................
3
2
DATA
AND
RELATIONS
.....................................................................................................
5
2.1
THE
IRIS
DATA
SET
.............................................................................................
5
2.2
DATA
SCALES
......................................................................................................
8
2.3
SET AND
MATRIX
REPRESENTATIONS
.....................................................................
10
2.4
RELATIONS
.........................................................................................................
11
2.5
DISSIMILARITY
MEASURES
..................................................................................
11
2.6
SIMILARITY
MEASURES
.......................................................................................
14
2.7
SEQUENCE
RELATIONS
........................................................................................
16
2.8
SAMPLING
AND
QUANTIZATION
...........................................................................
18
PROBLEMS
...................................................................................................................
21
REFERENCES
................................................................................................................
22
3
DATA
PREPROCESSING
....................................................................................................
23
3.1
ERROR
TYPES
....................................................................................................
23
3.2
ERROR
HANDLING
...............................................................................................
26
3.3
FILTERING
..........................................................................................................
27
3.4
DATA
TRANSFORMATION
.....................................................................................
33
3.5
DATA
INTEGRATION
.............................................................................................
35
PROBLEMS
...................................................................................................................
36
REFERENCES
................................................................................................................
36
4
DATA
VISUALIZATION
.......................................................................................................
37
4.1
DIAGRAMS
..........................................................................................................
37
4.2
PRINCIPAL
COMPONENT
ANALYSIS
.....................................................................
39
4.3
MULTIDIMENSIONAL
SCALING
..............................................................................
43
4.4
SAMMON
MAPPING
.........................................................................................
47
VII
VIII
CONTENTS
4.5
AUTO-ENCODER
..................................................................................................
51
4.6
HISTOGRAMS
......................................................................................................
51
4.7
SPECTRAL
ANALYSIS
............................................................................................
54
PROBLEMS
...................................................................................................................
58
REFERENCES
................................................................................................................
59
5
CORRELATION
....................................................................................................................
61
5.1
LINEAR
CORRELATION
...........................................................................................
61
5.2
CORRELATION
AND
CAUSALITY
..............................................................................
63
5.3
CHI-SQUARE
TEST
FOR
INDEPENDENCE
................................................................
64
PROBLEMS
...................................................................................................................
67
REFERENCES
................................................................................................................
68
6
REGRESSION
....................................................................................................................
69
6.1
LINEAR
REGRESSION
..........................................................................................
69
6.2
LINEAR
REGRESSION
WITH
NONLINEAR
SUBSTITUTION
...........................................
74
6.3
ROBUST
REGRESSION
..........................................................................................
75
6.4
NEURAL
NETWORKS
.............................................................................................
75
6.5
RADIAL
BASIS
FUNCTION
NETWORKS
...................................................................
81
6.6
CROSS-VALIDATION
.............................................................................................
82
6.7
FEATURE
SELECTION
.................................................................
85
PROBLEMS
...................................................................................................................
86
REFERENCES
................................................................................................................
87
7
FORECASTING
....................................................................................................................
89
7.1
FINITE
STATE
MACHINES
....................................................................................
89
7.2
RECURRENT
MODELS
............................................................................................
91
7.3
AUTOREGRESSIVE
MODELS
..................................................................................
92
PROBLEMS
...................................................................................................................
93
REFERENCES
................................................................................................................
94
8
CLASSIFICATION
...............................................................................................................
95
8.1
CLASSIFICATION
CRITERIA
....................................................................................
95
8.2
NAIVE
BAYES
CLASSIFIER
..................................................................................
99
8.3
LINEAR
DISCRIMINANT
ANALYSIS
........................................................................
102
8.4
SUPPORT
VECTOR
MACHINE
...............................................................................
104
8.5
NEAREST
NEIGHBOR
CLASSIFIER
...........................................................................
106
8.6
LEARNING
VECTOR
QUANTIZATION
........................................................................
107
8.7
DECISION
TREES
................................................................................................
108
PROBLEMS
...................................................................................................................
113
REFERENCES
................................................................................................................
114
CONTENTS
IX
9
CLUSTERING
.....................................................................................................................
117
9.1
CLUSTER
PARTITIONS
...........................................................................................
117
9.2
SEQUENTIAL
CLUSTERING
.....................................................................................
119
9.3
PROTOTYPE-BASED
CLUSTERING
...........................................................................
121
9.4
FUZZY
CLUSTERING
.............................................................................................
123
9.5
RELATIONAL
CLUSTERING
.....................................................................................
129
9.6
CLUSTER
TENDENCY
ASSESSMENT
......................................................................
133
9.7
CLUSTER
VALIDITY
..............................................................................................
134
9.8
SELF-ORGANIZING
MAP
.......................................................................................
135
PROBLEMS
...................................................................................................................
137
REFERENCES
................................................................................................................
137
A
BRIEF
REVIEW
OF
SOME
OPTIMIZATION
METHODS
.......................................................
141
A.L
OPTIMIZATION
WITH
DERIVATIVES
.....................................................................
141
A.
2
GRADIENT
DESCENT
..........................................................................................
142
A.3
LAGRANGE
OPTIMIZATION
.................................................................................
143
REFERENCES
................................................................................................................
145
SOLUTIONS
..............................................................................................................................
147
INDEX
.....................................................................................................................................
155
|
| adam_txt |
CONTENTS
1
INTRODUCTION
.
1
1.1
IT
*
S
ALL
ABOUT
DATA
.
1
1.2
DATA
ANALYTICS,
DATA MINING,
AND
KNOWLEDGE
DISCOVERY
.
2
REFERENCES
.
3
2
DATA
AND
RELATIONS
.
5
2.1
THE
IRIS
DATA
SET
.
5
2.2
DATA
SCALES
.
8
2.3
SET AND
MATRIX
REPRESENTATIONS
.
10
2.4
RELATIONS
.
11
2.5
DISSIMILARITY
MEASURES
.
11
2.6
SIMILARITY
MEASURES
.
14
2.7
SEQUENCE
RELATIONS
.
16
2.8
SAMPLING
AND
QUANTIZATION
.
18
PROBLEMS
.
21
REFERENCES
.
22
3
DATA
PREPROCESSING
.
23
3.1
ERROR
TYPES
.
23
3.2
ERROR
HANDLING
.
26
3.3
FILTERING
.
27
3.4
DATA
TRANSFORMATION
.
33
3.5
DATA
INTEGRATION
.
35
PROBLEMS
.
36
REFERENCES
.
36
4
DATA
VISUALIZATION
.
37
4.1
DIAGRAMS
.
37
4.2
PRINCIPAL
COMPONENT
ANALYSIS
.
39
4.3
MULTIDIMENSIONAL
SCALING
.
43
4.4
SAMMON
MAPPING
.
47
VII
VIII
CONTENTS
4.5
AUTO-ENCODER
.
51
4.6
HISTOGRAMS
.
51
4.7
SPECTRAL
ANALYSIS
.
54
PROBLEMS
.
58
REFERENCES
.
59
5
CORRELATION
.
61
5.1
LINEAR
CORRELATION
.
61
5.2
CORRELATION
AND
CAUSALITY
.
63
5.3
CHI-SQUARE
TEST
FOR
INDEPENDENCE
.
64
PROBLEMS
.
67
REFERENCES
.
68
6
REGRESSION
.
69
6.1
LINEAR
REGRESSION
.
69
6.2
LINEAR
REGRESSION
WITH
NONLINEAR
SUBSTITUTION
.
74
6.3
ROBUST
REGRESSION
.
75
6.4
NEURAL
NETWORKS
.
75
6.5
RADIAL
BASIS
FUNCTION
NETWORKS
.
81
6.6
CROSS-VALIDATION
.
82
6.7
FEATURE
SELECTION
.
85
PROBLEMS
.
86
REFERENCES
.
87
7
FORECASTING
.
89
7.1
FINITE
STATE
MACHINES
.
89
7.2
RECURRENT
MODELS
.
91
7.3
AUTOREGRESSIVE
MODELS
.
92
PROBLEMS
.
93
REFERENCES
.
94
8
CLASSIFICATION
.
95
8.1
CLASSIFICATION
CRITERIA
.
95
8.2
NAIVE
BAYES
CLASSIFIER
.
99
8.3
LINEAR
DISCRIMINANT
ANALYSIS
.
102
8.4
SUPPORT
VECTOR
MACHINE
.
104
8.5
NEAREST
NEIGHBOR
CLASSIFIER
.
106
8.6
LEARNING
VECTOR
QUANTIZATION
.
107
8.7
DECISION
TREES
.
108
PROBLEMS
.
113
REFERENCES
.
114
CONTENTS
IX
9
CLUSTERING
.
117
9.1
CLUSTER
PARTITIONS
.
117
9.2
SEQUENTIAL
CLUSTERING
.
119
9.3
PROTOTYPE-BASED
CLUSTERING
.
121
9.4
FUZZY
CLUSTERING
.
123
9.5
RELATIONAL
CLUSTERING
.
129
9.6
CLUSTER
TENDENCY
ASSESSMENT
.
133
9.7
CLUSTER
VALIDITY
.
134
9.8
SELF-ORGANIZING
MAP
.
135
PROBLEMS
.
137
REFERENCES
.
137
A
BRIEF
REVIEW
OF
SOME
OPTIMIZATION
METHODS
.
141
A.L
OPTIMIZATION
WITH
DERIVATIVES
.
141
A.
2
GRADIENT
DESCENT
.
142
A.3
LAGRANGE
OPTIMIZATION
.
143
REFERENCES
.
145
SOLUTIONS
.
147
INDEX
.
155 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Runkler, Thomas A. |
| author_GND | (DE-588)12200566X |
| author_facet | Runkler, Thomas A. |
| author_role | aut |
| author_sort | Runkler, Thomas A. |
| author_variant | t a r ta tar |
| building | Verbundindex |
| bvnumber | BV046811797 |
| classification_rvk | ST 515 ST 530 |
| classification_tum | DAT 825 DAT 600 DAT 703 |
| ctrlnum | (OCoLC)1190923579 (DE-599)DNB1205487832 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | Third edition |
| format | Book |
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| id | DE-604.BV046811797 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:59:01Z |
| indexdate | 2024-07-10T08:54:31Z |
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| institution_GND | (DE-588)1043386068 |
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| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-12 DE-20 |
| owner_facet | DE-91G DE-BY-TUM DE-12 DE-20 |
| physical | xii, 161 Seiten Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer Vieweg |
| record_format | marc |
| spelling | Runkler, Thomas A. Verfasser (DE-588)12200566X aut Information mining Data analytics models and algorithms for intelligent data analysis Thomas A. Runkler Third edition Wiesbaden Springer Vieweg 2020 xii, 161 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf classification deep learning business intelligence clustering algorithms forecasting data mining knowledge discovery machine learning (DE-588)4123623-3 Lehrbuch gnd-content Datenanalyse (DE-588)4123037-1 s Data Mining (DE-588)4428654-5 s DE-604 Springer Fachmedien Wiesbaden (DE-588)1043386068 pbl Erscheint auch als Online-Ausgabe 978-3-658-29779-4 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=a2371a78a6f848d6b931cae4914f7bff&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032220335&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Runkler, Thomas A. Data analytics models and algorithms for intelligent data analysis Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4123037-1 (DE-588)4123623-3 |
| title | Data analytics models and algorithms for intelligent data analysis |
| title_alt | Information mining |
| title_auth | Data analytics models and algorithms for intelligent data analysis |
| title_exact_search | Data analytics models and algorithms for intelligent data analysis |
| title_exact_search_txtP | Data analytics models and algorithms for intelligent data analysis |
| title_full | Data analytics models and algorithms for intelligent data analysis Thomas A. Runkler |
| title_fullStr | Data analytics models and algorithms for intelligent data analysis Thomas A. Runkler |
| title_full_unstemmed | Data analytics models and algorithms for intelligent data analysis Thomas A. Runkler |
| title_short | Data analytics |
| title_sort | data analytics models and algorithms for intelligent data analysis |
| title_sub | models and algorithms for intelligent data analysis |
| topic | Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Data Mining Datenanalyse Lehrbuch |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=a2371a78a6f848d6b931cae4914f7bff&prov=M&dok_var=1&dok_ext=htm http://www.springer.com/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032220335&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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