Advanced data mining techniques
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| Format: | Buch |
| Sprache: | English |
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Berlin [u.a.]
Springer
2008
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| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV023081838 | ||
| 003 | DE-604 | ||
| 005 | 20201106 | ||
| 007 | t | ||
| 008 | 080115s2008 gw d||| |||| 00||| eng d | ||
| 015 | |a 07,N47,0511 |2 dnb | ||
| 016 | 7 | |a 986189499 |2 DE-101 | |
| 020 | |a 9783540769163 |c Pb. : ca. EUR 80.20 (freier Pr.), ca. sfr 130.50 (freier Pr.) |9 978-3-540-76916-3 | ||
| 020 | |a 3540769161 |c Pb. : ca. EUR 80.20 (freier Pr.), ca. sfr 130.50 (freier Pr.) |9 3-540-76916-1 | ||
| 024 | 3 | |a 9783540769163 | |
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| 035 | |a (OCoLC)191760124 | ||
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| 100 | 1 | |a Olson, David L. |d 1944- |e Verfasser |0 (DE-588)1055798854 |4 aut | |
| 245 | 1 | 0 | |a Advanced data mining techniques |c David L. Olson ; Dursun Delen |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2008 | |
| 300 | |a XII, 180 S. |b graph. Darst. |c 235 mm x 155 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Exploration de données (Informatique) | |
| 650 | 4 | |a Data mining | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Delen, Dursun |e Verfasser |0 (DE-588)1136283463 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284848&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016284848 | ||
Datensatz im Suchindex
| _version_ | 1804137329953079297 |
|---|---|
| adam_text | Contents
Part I INTRODUCTION
1
Introduction
...............................................................................................3
What is Data Mining?
..........................................................................5
What is Needed to Do Data Mining
.....................................................5
Business Data Mining
..........................................................................7
Data Mining Tools
...............................................................................8
Summary
..............................................................................................8
2
Data Mining Process
.................................................................................9
CRISP-DM
..........................................................................................9
Business Understanding
.............................................................11
Data Understanding
...................................................................11
Data Preparation
........................................................................12
Modeling
...................................................................................15
Evaluation
..................................................................................18
Deployment
................................................................................18
SEMMA
.............................................................................................19
Steps in SEMMA Process
..........................................................20
Example Data Mining Process Application
.......................................22
Comparison of CRISP
&
SEMMA
....................................................27
Handling Data
....................................................................................28
Summary
............................................................................................34
Part II DATA MINING METHODS AS TOOLS
____________________
3
Memory-Based Reasoning Methods
.......................................................39
Matching
............................................................................................40
Weighted Matching
....................................................................43
Distance Minimization
.......................................................................44
Software
.............................................................................................50
Summary
............................................................................................50
Appendix: Job Application Data Set
..................................................51
X
Contents
4
Association
Rules in Knowledge Discovery
...........................................53
Market-Basket Analysis
.....................................................................55
Market Basket Analysis Benefits
...............................................56
Demonstration on Small Set of Data
.........................................57
Real Market Basket Data
...................................................................59
The Counting Method Without Software
..................................62
Conclusions
........................................................................................68
5
Fuzzy Sets in Data Mining
......................................................................69
Fuzzy Sets and Decision Trees
..........................................................71
Fuzzy Sets and Ordinal Classification
...............................................75
Fuzzy Association Rules
....................................................................79
Demonstration Model
................................................................80
Computational Results
...............................................................84
Testing
.......................................................................................84
Inferences
...................................................................................85
Conclusions
........................................................................................86
6
Rough Sets
..............................................................................................87
A Brief Theory of Rough Sets
...........................................................88
Information System
....................................................................88
Decision Table
...........................................................................89
Some Exemplary Applications of Rough Sets
...................................91
Rough Sets Software Tools
................................................................93
The Process of Conducting Rough Sets Analysis
..............................93
1
Data Pre-Processing
................................................................94
2
Data Partitioning
.....................................................................95
3
Discretization
..........................................................................95
4
Reduct Generation
..................................................................97
5
Rule Generation and Rule Filtering
........................................99
6
Apply the Discretization Cuts to Test
Dataset
......................100
7
Score the Test
Dataset
on Generated Rule set (and
measuring the prediction accuracy)
......................................100
8
Deploying the Rules in a Production System
.......................102
A Representative Example
...............................................................103
Conclusion
.......................................................................................109
7
Support Vector Machines
.....................................................................111
Formal Explanation of SVM
............................................................112
Primal Form
.............................................................................114
Contents
XI
Dual Form
................................................................................114
Soft Margin
..............................................................................114
Non-linear Classification
.................................................................115
Regression
................................................................................116
Implementation
........................................................................116
Kernel Trick
.............................................................................117
Use of SVM
-
A Process-Based Approach
.....................................118
Support Vector Machines versus Artificial Neural Networks
.........121
Disadvantages of Support Vector Machines
....................................122
8
Genetic Algorithm Support to Data Mining
.........................................125
Demonstration of Genetic Algorithm
..............................................126
Application of Genetic Algorithms in Data Mining
........................131
Summary
..........................................................................................132
Appendix: Loan Application Data Set
.............................................133
9
Performance Evaluation for Predictive Modeling
................................137
Performance Metrics for Predictive Modeling
................................137
Estimation Methodology for Classification Models
........................140
Simple Split (Holdout)
.....................................................................140
The ¿-Fold Cross Validation
............................................................141
Bootstrapping and
Jackknifíng
........................................................143
Area Under the ROC Curve
.............................................................144
Summary
..........................................................................................147
Part III APPLICATIONS
______________________________________
10
Applications of Methods
.....................................................................151
Memory-Based Application
.............................................................151
Association Rule Application
..........................................................153
Fuzzy Data Mining
..........................................................................155
Rough Set Models
............................................................................155
Support Vector Machine Application
..............................................157
Genetic Algorithm Applications
......................................................158
Japanese Credit Screening
.......................................................158
Product Quality Testing Design
...............................................159
Customer Targeting
.................................................................159
Medical Analysis
.....................................................................160
XII Contents
Predicting the Financial Success of Hollywood Movies
.................162
Problem and Data Description
.................................................163
Comparative Analysis of the Data Mining Methods
...............165
Conclusions
......................................................................................167
Bibliography
............................................................................................169
Index
........................................................................................................177
|
| adam_txt |
Contents
Part I INTRODUCTION
1
Introduction
.3
What is Data Mining?
.5
What is Needed to Do Data Mining
.5
Business Data Mining
.7
Data Mining Tools
.8
Summary
.8
2
Data Mining Process
.9
CRISP-DM
.9
Business Understanding
.11
Data Understanding
.11
Data Preparation
.12
Modeling
.15
Evaluation
.18
Deployment
.18
SEMMA
.19
Steps in SEMMA Process
.20
Example Data Mining Process Application
.22
Comparison of CRISP
&
SEMMA
.27
Handling Data
.28
Summary
.34
Part II DATA MINING METHODS AS TOOLS
_
3
Memory-Based Reasoning Methods
.39
Matching
.40
Weighted Matching
.43
Distance Minimization
.44
Software
.50
Summary
.50
Appendix: Job Application Data Set
.51
X
Contents
4
Association
Rules in Knowledge Discovery
.53
Market-Basket Analysis
.55
Market Basket Analysis Benefits
.56
Demonstration on Small Set of Data
.57
Real Market Basket Data
.59
The Counting Method Without Software
.62
Conclusions
.68
5
Fuzzy Sets in Data Mining
.69
Fuzzy Sets and Decision Trees
.71
Fuzzy Sets and Ordinal Classification
.75
Fuzzy Association Rules
.79
Demonstration Model
.80
Computational Results
.84
Testing
.84
Inferences
.85
Conclusions
.86
6
Rough Sets
.87
A Brief Theory of Rough Sets
.88
Information System
.88
Decision Table
.89
Some Exemplary Applications of Rough Sets
.91
Rough Sets Software Tools
.93
The Process of Conducting Rough Sets Analysis
.93
1
Data Pre-Processing
.94
2
Data Partitioning
.95
3
Discretization
.95
4
Reduct Generation
.97
5
Rule Generation and Rule Filtering
.99
6
Apply the Discretization Cuts to Test
Dataset
.100
7
Score the Test
Dataset
on Generated Rule set (and
measuring the prediction accuracy)
.100
8
Deploying the Rules in a Production System
.102
A Representative Example
.103
Conclusion
.109
7
Support Vector Machines
.111
Formal Explanation of SVM
.112
Primal Form
.114
Contents
XI
Dual Form
.114
Soft Margin
.114
Non-linear Classification
.115
Regression
.116
Implementation
.116
Kernel Trick
.117
Use of SVM
-
A Process-Based Approach
.118
Support Vector Machines versus Artificial Neural Networks
.121
Disadvantages of Support Vector Machines
.122
8
Genetic Algorithm Support to Data Mining
.125
Demonstration of Genetic Algorithm
.126
Application of Genetic Algorithms in Data Mining
.131
Summary
.132
Appendix: Loan Application Data Set
.133
9
Performance Evaluation for Predictive Modeling
.137
Performance Metrics for Predictive Modeling
.137
Estimation Methodology for Classification Models
.140
Simple Split (Holdout)
.140
The ¿-Fold Cross Validation
.141
Bootstrapping and
Jackknifíng
.143
Area Under the ROC Curve
.144
Summary
.147
Part III APPLICATIONS
_
10
Applications of Methods
.151
Memory-Based Application
.151
Association Rule Application
.153
Fuzzy Data Mining
.155
Rough Set Models
.155
Support Vector Machine Application
.157
Genetic Algorithm Applications
.158
Japanese Credit Screening
.158
Product Quality Testing Design
.159
Customer Targeting
.159
Medical Analysis
.160
XII Contents
Predicting the Financial Success of Hollywood Movies
.162
Problem and Data Description
.163
Comparative Analysis of the Data Mining Methods
.165
Conclusions
.167
Bibliography
.169
Index
.177 |
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| callnumber-search | QA76.9.D343 |
| callnumber-sort | QA 276.9 D343 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 270 ST 330 ST 530 |
| ctrlnum | (OCoLC)191760124 (DE-599)DNB986189499 |
| dewey-full | 006.312 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.312 |
| dewey-search | 006.312 |
| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
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| id | DE-604.BV023081838 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:37:29Z |
| indexdate | 2024-07-09T21:10:34Z |
| institution | BVB |
| isbn | 9783540769163 3540769161 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016284848 |
| oclc_num | 191760124 |
| open_access_boolean | |
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| owner_facet | DE-355 DE-BY-UBR DE-92 DE-703 DE-573 DE-83 DE-2070s |
| physical | XII, 180 S. graph. Darst. 235 mm x 155 mm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| spelling | Olson, David L. 1944- Verfasser (DE-588)1055798854 aut Advanced data mining techniques David L. Olson ; Dursun Delen Berlin [u.a.] Springer 2008 XII, 180 S. graph. Darst. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier Exploration de données (Informatique) Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Delen, Dursun Verfasser (DE-588)1136283463 aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284848&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Olson, David L. 1944- Delen, Dursun Advanced data mining techniques Exploration de données (Informatique) Data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Advanced data mining techniques |
| title_auth | Advanced data mining techniques |
| title_exact_search | Advanced data mining techniques |
| title_exact_search_txtP | Advanced data mining techniques |
| title_full | Advanced data mining techniques David L. Olson ; Dursun Delen |
| title_fullStr | Advanced data mining techniques David L. Olson ; Dursun Delen |
| title_full_unstemmed | Advanced data mining techniques David L. Olson ; Dursun Delen |
| title_short | Advanced data mining techniques |
| title_sort | advanced data mining techniques |
| topic | Exploration de données (Informatique) Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Exploration de données (Informatique) Data mining Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284848&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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