Descriptive data mining:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Singapore
Springer
[2019]
|
| Edition: | Second edition |
| Series: | Computational risk management
|
| Subjects: | |
| Online Access: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Physical Description: | xi, 130 Seiten Illustrationen, Diagramme 24 cm, 209 g |
| ISBN: | 9789811371806 |
Staff View
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| 250 | |a Second edition | ||
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| 653 | |a Business Analytics | ||
| 653 | |a Cluster Analysis | ||
| 653 | |a Data Mining | ||
| 653 | |a Descriptive Data Mining | ||
| 653 | |a Open Source Software | ||
| 653 | |a Visualization | ||
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Record in the Search Index
| _version_ | 1804181591126179840 |
|---|---|
| adam_text | CONTENTS
IX
1
KNOWLEDGE
MANAGEMENT
.............................................................................
1
COMPUTER
SUPPORT
SYSTEMS
...........................................................................
2
EXAMPLES
OF
KNOWLEDGE
MANAGEMENT
........................................................
4
DATA
MINING
DESCRIPTIVE
APPLICATIONS
........................................................
7
SUMMARY
..........................................................................................................
8
REFERENCES
.......................................................................................................
8
2
DATA
VISUALIZATION
.........................................................................................
11
DATA
VISUALIZATION
.........................................................................................
11
R
SOFTWARE
.......................................................................................................
12
LOAN
DATA
...................................................................................................
13
ENERGY
DATA
.....................................................................................................
20
BASIC
VISUALIZATION
OF
TIME
SERIES
.............................................................
21
CONCLUSION
.......................................................................................................
28
REFERENCES
.......................................................................................................
30
3
MARKET
BASKET
ANALYSIS
.............................................................................
31
DEFINITIONS
.......................................................................................................
32
CO-OCCURRENCE
................................................................................................
33
DEMONSTRATION
................................................................................................
37
FIT
................................................................................................................
38
PROFIT
............................................................................................................
38
LIFT
..............................................................................................................
41
MARKET
BASKET
LIMITATIONS
...........................................................................
43
REFERENCES
.......................................................................................................
44
4
RECENCY
FREQUENCY
AND
MONETARY
ANALYSIS
..........................................
45
DATASET
1
.........................................................................................................
46
BALANCING
CELLS
..............................................................................................
50
LIFT
...................................................................................................................
52
VALUE
FUNCTION
................................................................................................
53
X
CONTENTS
DATA
MINING
CLASSIFICATION
MODELS
.............................................................
58
LOGISTIC
REGRESSION
...................................................................................
58
DECISION
TREE
..............................................................................................
59
NEURAL
NETWORKS
.......................................................................................
59
DATASET
2
..........................................................................................................
59
CONCLUSIONS
.....................................................................................................
63
REFERENCES
.......................................................................................................
65
5
ASSOCIATION
RULES
.........................................................................................
67
METHODOLOGY
...................................................................................................
68
THE
APRIORI
ALGORITHM
..................................................................................
69
ASSOCIATION
RULES
FROM
SOFTWARE
..................................................................
71
NON-NEGATIVE
MATRIC
FACTORIZATION
................................................................
75
CONCLUSION
.......................................................................................................
76
REFERENCES
.......................................................................................................
76
6
CLUSTER
ANALYSIS
............................................................................................
77
K-MEANS
CLUSTERING
.......................................................................................
78
A
CLUSTERING
ALGORITHM
...........................................................................
78
LOAN
DATA
...................................................................................................
79
CLUSTERING
METHODS
USED
IN
SOFTWARE
........................................................
81
SOFTWARE
..........................................................................................................
82
R
(RATTLE)
K-MEANS
CLUSTERING
................................................................
82
OTHER
R
CLUSTERING
ALGORITHMS
................................................................
88
KNIME
.......................................................................................................
96
WEKA
.......................................................................................................
98
SUMMARY
..........................................................................................................
105
REFERENCES
.......................................................................................................
106
7
LINK
ANALYSIS
................................................................................................
107
LINK
ANALYSIS
TERMS
.......................................................................................
107
BASIC
NETWORK
GRAPHICS
WITH
NODEXL
......................................................
114
NETWORK
ANALYSIS
OF
FACEBOOK
NETWORK
OR
OTHER
NETWORKS
...................
118
LINK
ANALYSIS
OF
YOUR
EMAILS
.......................................................................
124
LINK
ANALYSIS
APPLICATION
WITH
POLY
ANALYST
(OLSON
AND
SHI
2007)
....
125
SUMMARY
..........................................................................................................
128
REFERENCES
.......................................................................................................
128
8
DESCRIPTIVE
DATA
MINING
..............................................................................
129
|
| adam_txt |
CONTENTS
IX
1
KNOWLEDGE
MANAGEMENT
.
1
COMPUTER
SUPPORT
SYSTEMS
.
2
EXAMPLES
OF
KNOWLEDGE
MANAGEMENT
.
4
DATA
MINING
DESCRIPTIVE
APPLICATIONS
.
7
SUMMARY
.
8
REFERENCES
.
8
2
DATA
VISUALIZATION
.
11
DATA
VISUALIZATION
.
11
R
SOFTWARE
.
12
LOAN
DATA
.
13
ENERGY
DATA
.
20
BASIC
VISUALIZATION
OF
TIME
SERIES
.
21
CONCLUSION
.
28
REFERENCES
.
30
3
MARKET
BASKET
ANALYSIS
.
31
DEFINITIONS
.
32
CO-OCCURRENCE
.
33
DEMONSTRATION
.
37
FIT
.
38
PROFIT
.
38
LIFT
.
41
MARKET
BASKET
LIMITATIONS
.
43
REFERENCES
.
44
4
RECENCY
FREQUENCY
AND
MONETARY
ANALYSIS
.
45
DATASET
1
.
46
BALANCING
CELLS
.
50
LIFT
.
52
VALUE
FUNCTION
.
53
X
CONTENTS
DATA
MINING
CLASSIFICATION
MODELS
.
58
LOGISTIC
REGRESSION
.
58
DECISION
TREE
.
59
NEURAL
NETWORKS
.
59
DATASET
2
.
59
CONCLUSIONS
.
63
REFERENCES
.
65
5
ASSOCIATION
RULES
.
67
METHODOLOGY
.
68
THE
APRIORI
ALGORITHM
.
69
ASSOCIATION
RULES
FROM
SOFTWARE
.
71
NON-NEGATIVE
MATRIC
FACTORIZATION
.
75
CONCLUSION
.
76
REFERENCES
.
76
6
CLUSTER
ANALYSIS
.
77
K-MEANS
CLUSTERING
.
78
A
CLUSTERING
ALGORITHM
.
78
LOAN
DATA
.
79
CLUSTERING
METHODS
USED
IN
SOFTWARE
.
81
SOFTWARE
.
82
R
(RATTLE)
K-MEANS
CLUSTERING
.
82
OTHER
R
CLUSTERING
ALGORITHMS
.
88
KNIME
.
96
WEKA
.
98
SUMMARY
.
105
REFERENCES
.
106
7
LINK
ANALYSIS
.
107
LINK
ANALYSIS
TERMS
.
107
BASIC
NETWORK
GRAPHICS
WITH
NODEXL
.
114
NETWORK
ANALYSIS
OF
FACEBOOK
NETWORK
OR
OTHER
NETWORKS
.
118
LINK
ANALYSIS
OF
YOUR
EMAILS
.
124
LINK
ANALYSIS
APPLICATION
WITH
POLY
ANALYST
(OLSON
AND
SHI
2007)
.
125
SUMMARY
.
128
REFERENCES
.
128
8
DESCRIPTIVE
DATA
MINING
.
129 |
| any_adam_object | 1 |
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| author | Olson, David L. 1944- Lauhoff, Georg |
| author_GND | (DE-588)1055798854 (DE-588)1191718417 |
| author_facet | Olson, David L. 1944- Lauhoff, Georg |
| author_role | aut aut |
| author_sort | Olson, David L. 1944- |
| author_variant | d l o dl dlo g l gl |
| building | Verbundindex |
| bvnumber | BV046796648 |
| classification_rvk | ST 530 |
| ctrlnum | (OCoLC)1049595175 (DE-599)DNB1164662201 |
| dewey-full | 658.4038 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4038 |
| dewey-search | 658.4038 |
| dewey-sort | 3658.4038 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV046796648 |
| illustrated | Illustrated |
| index_date | 2024-07-03T14:54:42Z |
| indexdate | 2024-07-10T08:54:05Z |
| institution | BVB |
| institution_GND | (DE-588)1065365012 |
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| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032205493 |
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| open_access_boolean | |
| owner | DE-N2 |
| owner_facet | DE-N2 |
| physical | xi, 130 Seiten Illustrationen, Diagramme 24 cm, 209 g |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Springer |
| record_format | marc |
| series2 | Computational risk management |
| spelling | Olson, David L. 1944- (DE-588)1055798854 aut Descriptive data mining David L. Olson, Georg Lauhoff Second edition Singapore Springer [2019] © 2019 xi, 130 Seiten Illustrationen, Diagramme 24 cm, 209 g txt rdacontent n rdamedia nc rdacarrier Computational risk management Big Data (DE-588)4802620-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Wissensmanagement (DE-588)4561842-2 gnd rswk-swf Risikomanagement (DE-588)4121590-4 gnd rswk-swf Big Data Business Analytics Cluster Analysis Data Mining Descriptive Data Mining Open Source Software Visualization Wissensmanagement (DE-588)4561842-2 s Risikomanagement (DE-588)4121590-4 s Data Mining (DE-588)4428654-5 s Big Data (DE-588)4802620-7 s DE-604 Lauhoff, Georg Verfasser (DE-588)1191718417 aut Springer Malaysia Representative Office (DE-588)1065365012 pbl Erscheint auch als Online-Ausgabe 978-981-13-7181-3 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=86ebcbfd09a14c8fa7973cc7a1f3aa6c&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=032205493&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Olson, David L. 1944- Lauhoff, Georg Descriptive data mining Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Wissensmanagement (DE-588)4561842-2 gnd Risikomanagement (DE-588)4121590-4 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4428654-5 (DE-588)4561842-2 (DE-588)4121590-4 |
| title | Descriptive data mining |
| title_auth | Descriptive data mining |
| title_exact_search | Descriptive data mining |
| title_exact_search_txtP | Descriptive data mining |
| title_full | Descriptive data mining David L. Olson, Georg Lauhoff |
| title_fullStr | Descriptive data mining David L. Olson, Georg Lauhoff |
| title_full_unstemmed | Descriptive data mining David L. Olson, Georg Lauhoff |
| title_short | Descriptive data mining |
| title_sort | descriptive data mining |
| topic | Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Wissensmanagement (DE-588)4561842-2 gnd Risikomanagement (DE-588)4121590-4 gnd |
| topic_facet | Big Data Data Mining Wissensmanagement Risikomanagement |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=86ebcbfd09a14c8fa7973cc7a1f3aa6c&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=032205493&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT olsondavidl descriptivedatamining AT lauhoffgeorg descriptivedatamining AT springermalaysiarepresentativeoffice descriptivedatamining |