Advanced data mining techniques:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2008
|
| Schlagwörter: | |
| Links: | 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 |
| Umfang: | XII, 180 S. graph. Darst. 235 mm x 155 mm |
| ISBN: | 9783540769163 3540769161 |
Internformat
MARC
| 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 | |
| 028 | 5 | 2 | |a 12195442 |
| 035 | |a (OCoLC)191760124 | ||
| 035 | |a (DE-599)DNB986189499 | ||
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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 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-016284848 | |
Datensatz im Suchindex
| _version_ | 1819361961516728320 |
|---|---|
| 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
|
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| 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 |
| format | Book |
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| id | DE-604.BV023081838 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:08:12Z |
| institution | BVB |
| isbn | 9783540769163 3540769161 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016284848 |
| oclc_num | 191760124 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-92 DE-703 DE-573 DE-83 DE-2070s |
| 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 |
| 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_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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