Data analytics: models and algorithms for intelligent data analysis
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Wiesbaden
Springer Vieweg
2020
|
| Edition: | Third edition |
| Subjects: | |
| Online Access: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Physical Description: | xii, 161 Seiten Diagramme |
| ISBN: | 9783658297787 |
Staff View
MARC
| LEADER | 00000nam a22000008c 4500 | ||
|---|---|---|---|
| 001 | BV046811797 | ||
| 003 | DE-604 | ||
| 005 | 20210302 | ||
| 007 | t | ||
| 008 | 200717s2020 gw |||| |||| 00||| eng d | ||
| 015 | |a 20,N10 |2 dnb | ||
| 016 | 7 | |a 1205487832 |2 DE-101 | |
| 020 | |a 9783658297787 |c Festeinband : circa EUR 48.14 (DE) (freier Preis), circa EUR 49.49 (AT) (freier Preis), circa CHF 53.50 (freier Preis) |9 978-3-658-29778-7 | ||
| 024 | 3 | |a 9783658297787 | |
| 028 | 5 | 2 | |a Bestellnummer: 978-3-658-29778-7 |
| 028 | 5 | 2 | |a Bestellnummer: 89074413 |
| 035 | |a (OCoLC)1190923579 | ||
| 035 | |a (DE-599)DNB1205487832 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-HE | ||
| 049 | |a DE-91G |a DE-12 |a DE-20 | ||
| 084 | |a ST 515 |0 (DE-625)143677: |2 rvk | ||
| 084 | |a ST 530 |0 (DE-625)143679: |2 rvk | ||
| 084 | |a DAT 825 |2 stub | ||
| 084 | |a DAT 600 |2 stub | ||
| 084 | |a 004 |2 sdnb | ||
| 084 | |a DAT 703 |2 stub | ||
| 100 | 1 | |a Runkler, Thomas A. |e Verfasser |0 (DE-588)12200566X |4 aut | |
| 240 | 1 | 0 | |a Information mining |
| 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 | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 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 | ||
| 655 | 7 | |0 (DE-588)4123623-3 |a Lehrbuch |2 gnd-content | |
| 689 | 0 | 0 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
| 689 | 0 | 1 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 710 | 2 | |a Springer Fachmedien Wiesbaden |0 (DE-588)1043386068 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-658-29779-4 |
| 856 | 4 | 2 | |m X:MVB |q text/html |u http://deposit.dnb.de/cgi-bin/dokserv?id=a2371a78a6f848d6b931cae4914f7bff&prov=M&dok_var=1&dok_ext=htm |3 Inhaltstext |
| 856 | 4 | 2 | |m X:MVB |u http://www.springer.com/ |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u 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 |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032220335 | ||
Record in the Search Index
| _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 |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02555nam a22006618c 4500</leader><controlfield tag="001">BV046811797</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20210302 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">200717s2020 gw |||| |||| 00||| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">20,N10</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1205487832</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783658297787</subfield><subfield code="c">Festeinband : circa EUR 48.14 (DE) (freier Preis), circa EUR 49.49 (AT) (freier Preis), circa CHF 53.50 (freier Preis)</subfield><subfield code="9">978-3-658-29778-7</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9783658297787</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">Bestellnummer: 978-3-658-29778-7</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">Bestellnummer: 89074413</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1190923579</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1205487832</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-HE</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield><subfield code="a">DE-12</subfield><subfield code="a">DE-20</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 515</subfield><subfield code="0">(DE-625)143677:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 530</subfield><subfield code="0">(DE-625)143679:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 825</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 600</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">004</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 703</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Runkler, Thomas A.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)12200566X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="240" ind1="1" ind2="0"><subfield code="a">Information mining</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data analytics</subfield><subfield code="b">models and algorithms for intelligent data analysis</subfield><subfield code="c">Thomas A. Runkler</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Third edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Wiesbaden</subfield><subfield code="b">Springer Vieweg</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xii, 161 Seiten</subfield><subfield code="b">Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">classification</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">deep learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">business intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">clustering</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">algorithms</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">data mining</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">knowledge discovery</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine learning</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4123623-3</subfield><subfield code="a">Lehrbuch</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">Springer Fachmedien Wiesbaden</subfield><subfield code="0">(DE-588)1043386068</subfield><subfield code="4">pbl</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-658-29779-4</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">X:MVB</subfield><subfield code="q">text/html</subfield><subfield code="u">http://deposit.dnb.de/cgi-bin/dokserv?id=a2371a78a6f848d6b931cae4914f7bff&prov=M&dok_var=1&dok_ext=htm</subfield><subfield code="3">Inhaltstext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">X:MVB</subfield><subfield code="u">http://www.springer.com/</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">DNB Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">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</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032220335</subfield></datafield></record></collection> |
| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV046811797 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:59:01Z |
| indexdate | 2024-07-10T08:54:31Z |
| institution | BVB |
| institution_GND | (DE-588)1043386068 |
| isbn | 9783658297787 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032220335 |
| oclc_num | 1190923579 |
| 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 |
| work_keys_str_mv | AT runklerthomasa informationmining AT springerfachmedienwiesbaden informationmining AT runklerthomasa dataanalyticsmodelsandalgorithmsforintelligentdataanalysis AT springerfachmedienwiesbaden dataanalyticsmodelsandalgorithmsforintelligentdataanalysis |