Data mining: Special issue in annals of information systems
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
New York [u.a.]
Springer
2010
|
| Series: | Annals of Information Systems
8 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references. |
| Physical Description: | XIII, 387 S. graph. Darst. |
| ISBN: | 9781441912794 9781441912800 |
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MARC
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| 245 | 1 | 0 | |a Data mining |b Special issue in annals of information systems |c Robert Stahlbock ... eds. |
| 264 | 1 | |a New York [u.a.] |b Springer |c 2010 | |
| 300 | |a XIII, 387 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a Annals of Information Systems |v 8 | |
| 500 | |a Includes bibliographical references. | ||
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| adam_text | Contents
1 Data Mining and Information Systems: Quo
Vadis?
............... 1
Robert Stahlbock, Stefan Lessmann, and Sven F. Crone
1.1
Introduction
.............................................. 1
1.2 Special
Issues
in Data Mining............................... 3
1.2.1
Confirmatory Data Analysis
......................... 3
1.2.2 Knowledge Discovery
from Supervised Learning
....... 4
1.2.3
Classification Analysis
............................. 6
1.2.4
Hybrid Data Mining Procedures
..................... 8
1.2.5
Web Mining
...................................... 10
1.2.6
Privacy-Preserving Data Mining
..................... 11
1.3
Conclusion and Outlook
.................................... 12
References
..................................................... 13
Part I Confirmatory Data Analysis
2
Response-Based Segmentation Using Finite Mixture Partial Least
Squares
.................................................... 19
Christian M.
Ringle, Marko
Sarstedt, and Erik
Α.
Mooi
2.1
Introduction
.............................................. 20
2.1.1
On the Use of PLS Path Modeling
................... 20
2.1.2
Problem Statement
................................ 22
2.1.3
Objectives and Organization
........................ 23
2.2
Partial Least Squares Path Modeling
......................... 24
2.3
Finite Mixture Partial Least Squares Segmentation
............. 26
2.3.1
Foundations
...................................... 26
2.3.2
Methodology
..................................... 28
2.3.3
Systematic Application of FIMIX-PLS
................ 31
2.4
Application of FIMIX-PLS
................................. 34
2.4.1
On Measuring Customer Satisfaction
................. 34
2.4.2
Data and Measures
................................ 34
2.4.3
Data Analysis and Results
.......................... 36
Yjji Contents
2.5
Summary and Conclusion
.................................. 44
References
..................................................... 45
Part II Knowledge Discovery from Supervised Learning
3
Building Acceptable Classification Models
...................... 53
David Martens and Bart Baesens
3.1
Introduction
.............................................. 54
3.2
Comprehensibility of Classification Models
................... 55
3.2.1
Measuring Comprehensibility
....................... 57
3.2.2
Obtaining Comprehensible Classification Models
....... 58
3.3
Justifiability of Classification Models
......................... 59
3.3.1
Taxonomy of Constraints
........................... 60
3.3.2
Monotonicity Constraint
............................ 62
3.3.3
Measuring Justifiability
............................ 63
3.3.4
Obtaining Justifiable Classification Models
............ 68
3.4
Conclusion
............................................... 70
References
..................................................... 71
4
Mining Interesting Rules Without Support Requirement: A
General Universal Existential Upward Closure Property
.......... 75
Yannick Le Bras,
Philippe
Lenca,
and
Stéphane
Lallich
4.1
Introduction
.............................................. 76
4.2
State of the Art
........................................... 77
4.3
An Algorithmic Property of Confidence
...................... 80
4.3.1
On UEUC Framework
............................. 80
4.3.2
The UEUC Property
............................... 80
4.3.3
An Efficient Pruning Algorithm
...................... 81
4.3.4
Generalizing the UEUC Property
.................... 82
4.4
A Framework for the Study of Measures
...................... 84
4.4.1
Adapted Functions of Measure
...................... 84
4.4.2
Expression of a Set of Measures of Diamf
............. 87
4.5
Conditions for GUEUC
.................... ................ 90
4.5.1
A Sufficient Condition
............................. 90
4.5.2
A Necessary Condition
............................. 91
4.5.3
Classification of the Measures
....................... 92
4.6
Conclusion
............................................... 94
References
..................................................... 95
5
Classification Techniques and Error Control in Logic Mining
...... 99
Giovanni
Felici,
Bruno
Simeoné,
and
Vincenzo Spinelli
5.1
Introduction
.............................................. 100
5.2
Brief Introduction to Box Clustering
......................... 102
5.3
ВС
-Based Classifier
....................................... 104
5.4
Best Choice of a Box System
............................... 108
5.5
Bi-criterion Procedure for
ßC-Based
Classifier
.................
Ill
Contents ix
5.6
Examples
................................................112
5.6.1
The Data Sets
.....................................112
5.6.2
Experimental Results with
ВС
.......................113
5.6.3
Comparison with Decision Trees
.....................115
5.7
Conclusions
..............................................117
References
.....................................................117
Part III Classification Analysis
6
An Extended Study of the Discriminant Random Forest
...........123
Tracy D. Lemmond, Barry Y. Chen, Andrew O. Hatch,
and William G. Hanley
6.1
Introduction
..............................................123
6.2
Random Forests
..........................................124
6.3
Discriminant Random Forests
...............................125
6.3.1
Linear Discriminant Analysis
.......................126
6.3.2
The Discriminant Random Forest Methodology
........127
6.4
DRF and RF: An Empirical Study
...........................128
6.4.1
Hidden Signal Detection
............................129
6.4.2
Radiation Detection
................................132
6.4.3
Significance of Empirical Results
....................136
6.4.4
Small Samples and Early Stopping
...................137
6.4.5
Expected Cost
....................................143
6.5
Conclusions
..............................................143
References
.....................................................145
7
Prediction with the SVM Using Test Point Margins
...............147
Süreyya Özögür-Akyüz,
Zakria Hussain, and John Shawe-Taylor
7.1
Introduction
..............................................147
7.2
Methods
.................................................151
7.3
Data Set Description
.......................................154
7.4
Results
..................................................154
7.5
Discussion and Future Work
................................
І55
References
.....................................................157
8
Effects of Oversampling Versus Cost-Sensitive Learning for
Bayesian and SVM Classifiers
................................. 159
Alexander Liu, Cheryl Martin, Brian
La Cour,
and Joydeep Ghosh
8.1
Introduction
..............................................159
8.2
Resampling
..............................................161
8.2.1
Random Oversampling
.............................161
8.2.2
Generative Oversampling
...........................161
8.3
Cost-Sensitive Learning
....................................162
8.4
Related Work
.............................................163
8.5
A Theoretical Analysis of Oversampling Versus Cost-Sensitive
Learning
.................................................164
x
Contents
8.5.1
Bayesian
Classification
.............................164
8.5.2
Resampling Versus Cost-Sensitive Learning in
Bayesian Classifiers
...............................165
8.5.3
Effect of Oversampling on Gaussian Naive
Bayes
......166
8.5.4
Effects of Oversampling for Multinomial Naive
Bayes
.. 168
8.6
Empirical Comparison of Resampling and Cost-Sensitive
Learning
.................................................170
8.6.1
Explaining Empirical Differences Between Resampling
and Cost-Sensitive Learning
........................170
8.6.2
Naive
Bayes
Comparisons on Low-Dimensional
Gaussian Data
....................................171
8.6.3
Multinomial Naive
Bayes
...........................176
8.6.4
SVMs
...........................................178
8.6.5
Discussion
.......................................181
8.7
Conclusion
...............................................182
Appendix
......................................................183
References
.....................................................190
9
The Impact of Small
Disjuncte
on Classifier Learning
............. 193
Gary M. Weiss
9.1
Introduction
..............................................193
9.2
An Example: The Vote Data Set
.............................195
9.3
Description of Experiments
.................................197
9.4
The Problem with Small Disjuncts
...........................198
9.5
The Effect of Pruning on Small Disjuncts
.....................202
9.6
The Effect of Training Set Size on Small Disjuncts
.............210
9.7
The Effect of Noise on Small Disjuncts
.......................213
9.8
The Effect of Class Imbalance on Small Disjuncts
..............217
9.9
Related Work
.............................................220
9.10
Conclusion
...............................................223
References
.....................................................225
Part IV Hybrid Data Mining Procedures
10
Predicting Customer Loyalty Labels in a Large Retail Database: A
Case Study in Chile
..........................................229
Cristian
J.
Figueroa
10.1
Introduction
..............................................229
10.2
Related Work
.............................................231
10.3
Objectives of the Study
....................................233
10.3.1
Supervised and Unsupervised Learning
...............234
10.3.2
Unsupervised Algorithms
...........................234
10.3.3
Variables for Segmentation
.........................238
10.3.4
Exploratory Data Analysis
..........................239
10.3.5
Results of the Segmentation
.........................240
10.4
Results of the Classifier
....................................241
Contents xi
10.5 Business
Validation .......................................
244
10.5.1
In-Store
Minutes Charges
for Prepaid Cell Phones
......245
10.5.2
Distribution
of
Products
in the Store
..................246
10.6
Conclusions and Discussion
................................248
Appendix
......................................................250
References
.....................................................252
11
PCA-Based Time Series Similarity Search
.......................255
Leónidas Karamitopoulos,
Georgios Evangelidis, and Dimitris Dervos
11.1
Introduction
..............................................256
11.2
Background
..............................................258
11.2.1
Review of PCA
...................................258
11.2.2
Implications of PCA in Similarity Search
.............259
11.23
Related Work
.....................................261
11.3
Proposed Approach
.......................................263
11.4
Experimental Methodology
.................................265
.4.1
Data Sets
........................................265
.4.2
Evaluation Methods
...............................266
.4,3
Rival Measures
...................................267
11.5
Results
..................................................268
.5.1
1-NN Classification
................................268
.5.2
k-NN Similarity Search
............................271
11.5.3
Speeding Up the Calculation of APEdist
..............272
11.6
Conclusion
...............................................274
References
.....................................................274
12
Evolutionary Optimization of Least-Squares Support Vector
Machines
..................................................277
Arjan Gijsberts, Giorgio
Metta,
and
Léon
Rothkrantz
12.1
Introduction
..............................................278
12.2
Kernel Machines
..........................................278
12.2.1
Least-Squares Support Vector Machines
..............279
12.2.2
Kernel Functions
..................................280
12.3
Evolutionary Computation
..................................281
12.3.1
Genetic Algorithms
................................281
12.3.2
Evolution Strategies
...............................282
12.3.3
Genetic Programming
..............................283
12.4
Related Work
.............................................283
12.4.1
Hyperparameter Optimization
.......................284
12.4.2
Combined Kernel Functions
.........................284
12.5
Evolutionary Optimization of Kernel Machines
................286
12.5.1
Hyperparameter Optimization
.......................286
12.5.2
Kernel Construction
...............................287
12.5.3
Objective Function
................................288
12.6
Results
..................................................289
12.6.1
Data Sets
........................................289
Contents
12.6.2
Results for Hyperparameter Optimization
.............290
12.6.3
Results for EvoKM01
..............................293
12.7
Conclusions and Future Work
...............................294
References
.....................................................295
13
Genetically Evolved kNN Ensembles
...........................299
Ulf
Johansson,
Rikard
König,
and Lars Niklasson
13.1
Introduction
..............................................299
13.2
Background and Related Work
..............................301
13.3
Method
..................................................302
13.3.1
Data sets
.........................................305
13.4
Results
..................................................307
13.5
Conclusions
..............................................312
References
.....................................................313
PartV Web-Mining
14
Behaviorally Founded Recommendation Algorithm for Browsing
Assistance Systems
..........................................317
Peter
Géczy,
Noriaki
Izumi,
Shotaro Akaho, and
Kôïti
Hasida
14.1
Introduction
..............................................317
14.1.1
Related Works
....................................318
14.1.2
Our Contribution and Approach
.....................319
14.2
Concept Formalization
.....................................319
14.3
System Design
...........................................323
14.3.1
A Priori Knowledge of Human-System Interactions
.... 323
14.3.2
Strategic Design Factors
............................323
14.3.3
Recommendation Algorithm Derivation
...............325
14.4
Practical Evaluation
.......................................327
14.4.1
Intranet Portal
....................................328
14.4.2
System Evaluation
.................................330
14.4.3
Practical Implications and Limitations
................331
14.5
Conclusions and Future Work
...............................332
References
.....................................................333
15
Using Web Text Mining to Predict Future Events: A Test
of the Wisdom of Crowds Hypothesis
...........................335
Scott Ryan and
Lutz
Hamel
15.1
Introduction
..............................................335
15.2
Method
..................................................337
15.2.1
Hypotheses and Goals
..............................337
15.2.2
General Methodology
..............................339
15.2.3
The
2006
Congressional and Gubernatorial Elections
___339
15.2.4
Sporting Events and Reality Television Programs
.......340
15.2.5
Movie Box Office Receipts and Music Sales
...........341
15.2.6
Replication
................................... 342
Contents xiii
15.3
Results and Discussion
.....................................343
15.3.1
The
2006
Congressional and Gubernatorial Elections
___343
15.3.2
Sporting Events and Reality Television Programs
.......345
15.3.3
Movie and Music Album Results
....................347
15.4
Conclusion
...............................................348
References
.....................................................349
Part VI Privacy-Preserving Data Mining
16
Avoiding Attribute Disclosure with the (Extended) /»-Sensitive
Ä-Anonymity
Model
.........................................353
Traían
Marius
Truta
and
Alina Campan
16.1
Introduction
..............................................353
16.2
Privacy Models and Algorithms
.............................354
16.2.1
The ^-Sensitive ¿-Anonymity Model and Its Extension
.. 354
16.2.2
Algorithms for the p-Sensitive
Л
-Anonymity
Model
.....357
16.3
Experimental Results
......................................360
16.3.1
Experiments for^-Sensitive ¿-Anonymity
............360
16.3.2
Experiments for Extended p-Sensitive ¿-Anonymity
___362
16.4
New Enhanced Models Based on p-Sensitive ¿-Anonymity
......366
16.4.1
Constrained p-Sensitive ¿-Anonymity
................366
16.4.2
p-Sensitive ¿-Anonymity in Social Networks
..........370
16.5
Conclusions and Future Work
...............................372
References
.....................................................372
17
Privacy-Preserving Random Kernel Classification of Checkerboard
Partitioned Data
............................................375
Olvi L. Mangasarian and Edward W. Wild
17.1
Introduction
..............................................375
17.2
Privacy-Preserving Linear Classifier for Checkerboard
Partitioned Data
..........................................379
17.3
Privacy-Preserving Nonlinear Classifier for Checkerboard
Partitioned Data
..........................................381
17.4
Computational Results
.....................................382
17.5
Conclusion and Outlook
....................................384
References
.....................................................386
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV035682323 |
| illustrated | Illustrated |
| indexdate | 2025-04-04T04:01:31Z |
| institution | BVB |
| isbn | 9781441912794 9781441912800 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017736576 |
| oclc_num | 551161869 |
| open_access_boolean | |
| owner | DE-29 DE-355 DE-BY-UBR DE-945 DE-863 DE-BY-FWS DE-2070s |
| owner_facet | DE-29 DE-355 DE-BY-UBR DE-945 DE-863 DE-BY-FWS DE-2070s |
| physical | XIII, 387 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
| record_format | marc |
| series | Annals of Information Systems |
| series2 | Annals of Information Systems |
| spellingShingle | Data mining Special issue in annals of information systems Annals of Information Systems Data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4143413-4 |
| title | Data mining Special issue in annals of information systems |
| title_auth | Data mining Special issue in annals of information systems |
| title_exact_search | Data mining Special issue in annals of information systems |
| title_full | Data mining Special issue in annals of information systems Robert Stahlbock ... eds. |
| title_fullStr | Data mining Special issue in annals of information systems Robert Stahlbock ... eds. |
| title_full_unstemmed | Data mining Special issue in annals of information systems Robert Stahlbock ... eds. |
| title_short | Data mining |
| title_sort | data mining special issue in annals of information systems |
| title_sub | Special issue in annals of information systems |
| topic | Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data mining Data Mining Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736576&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV022718881 |
| work_keys_str_mv | AT stahlbockrobert dataminingspecialissueinannalsofinformationsystems |
Indexes
THWS Würzburg Teilbibliothek SHL, Raum I.2.11
| Call Number: |
1340 ST 530 S781st |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |