Data mining for business applications:
'Data Mining for Business Applications' presents the state-of-the-art research & development outcomes on methodologies, techniques, approaches & successful applications in the area.
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
New York
Springer
2009
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | 'Data Mining for Business Applications' presents the state-of-the-art research & development outcomes on methodologies, techniques, approaches & successful applications in the area. |
| Physical Description: | XIX, 302 S. Ill., graph. Darst., Kt. 235 mm x 155 mm |
| ISBN: | 9780387794198 |
Staff View
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| 245 | 1 | 0 | |a Data mining for business applications |c ed. by Longbing Cao ... |
| 264 | 1 | |a New York |b Springer |c 2009 | |
| 300 | |a XIX, 302 S. |b Ill., graph. Darst., Kt. |c 235 mm x 155 mm | ||
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| 520 | 3 | |a 'Data Mining for Business Applications' presents the state-of-the-art research & development outcomes on methodologies, techniques, approaches & successful applications in the area. | |
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| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Business |x Data processing | |
| 650 | 4 | |a Data mining | |
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Record in the Search Index
| _version_ | 1804138647999479808 |
|---|---|
| adam_text | Contents
Part I Domain Driven KDD Methodology
1
Introduction to Domain Driven Data Mining
.................... 3
Longbing
Cao
1.1
Why Domain Driven Data Mining
........................... 3
1.2
What Is Domain Driven Data Mining
......................... 5
1.2.1
Basic Ideas
....................................... 5
1.2.2
D3M
for Actionable Knowledge Discovery
............ 6
1.3
Open Issues and Prospects
.................................. 9
1.4
Conclusions
.............................................. 9
References
..................................................... 10
2
Post-processing Data Mining Models for Actionability
............ 11
Qiang Yang
2.1
Introduction
.............................................. 11
2.2
Plan Mining for Class Transformation
........................ 12
2.2.1
Overview of Plan Mining
.......................... 12
2.2.2
Problem Formulation
.............................. 14
2.2.3
From Association Rules to State Spaces
............... 14
2.2.4
Algorithm for Plan Mining
.......................... 17
2.2.5
Summary
........................................ 19
2.3
Extracting Actions from Decision Trees
...................... 20
2.3.1
Overview
........................................ 20
2.3.2
Generating Actions from Decision Trees
.............. 22
2.3.3
The Limited Resources Case
........................ 23
2.4
Learning Relational Action Models from Frequent Action
Sequences
............................................... 25
2.4.1
Overview
........................................ 25
2.4.2
ARMS Algorithm: From Association Rules to Actions
.. 26
2.4.3
Summary of ARMS
............................... 28
2.5
Conclusions and Future Work
............................... 29
Contents
References
..................................................... 29
On Mining Maxima] Pattern-Based Clusters
..................... 31
Jian
Peí, Xiaoling
Zhang, Moonjung Cho, Haixun Wang, and Philip
S.Yu
3.1
Introduction
.............................................. 32
3.2
Problem Definition and Related Work
........................ 34
3.2.1
Pattern-Based Clustering
........................... 34
3.2.2
Maximal Pattern-Based Clustering
................... 35
3.2.3
Related Work
..................................... 35
3.3
Algorithms MaPle and MaPle*
............................. 36
3.3.1
An Overview of MaPle
............................ 37
3.3.2
Computing and Pruning MDS s
...................... 38
3.3.3
Progressively Refining, Depth-first Search of Maximal
pClusters
........................................ 40
3.3.4
MaPle+ .
Further Improvements
...................... 44
3.4
Empirical Evaluation
...................................... 46
3.4.1
The Data Sets
..................................... 46
3.4.2
Results on Yeast Data Set
........................... 47
3.4.3
Results on Synthetic Data Sets
...................... 48
3.5
Conclusions
.............................................. 50
References
..................................................... 50
Role of Human Intelligence in Domain Driven Data Mining
........ 53
Sumana Sharma
and Kweku-Muata Osei-Bryson
4.1
Introduction
.............................................. 53
4.2
DDDM Tasks Requiring Human Intelligence
.................. 54
4.2.1
Formulating Business Objectives
.................... 54
4.2.2
Setting up Business Success Criteria
.................. 55
4.2.3
Translating Business Objective to Data Mining Objectives
56
4.2.4
Setting up of Data Mining Success Criteria
............ 56
4.2.5
Assessing Similarity Between Business Objectives of
New and Past Projects
.............................. 57
4.2.6
Formulating Business, Legal and Financial
Requirements
.................................... 57
4.2.7
Narrowing down Data and Creating Derived Attributes
.. 58
4.2.8
Estimating Cost of Data Collection, Implementation
and Operating Costs
............................... 58
4.2.9
Selection of Modeling Techniques
................... 59
4.2.10
Setting up Model Parameters
........................ 59
4.2.11
Assessing Modeling Results
........................ 59
4.2.12
Developing a Project Plan
.......................... 60
4.3
Directions for Future Research
.............................. 60
4.4
Summary
................................................ 61
References
..................................................... 61
Contents ¡x
5
Ontology
Mining
for Personalized Search
....................... 63
Yuefeng Li and Xiaohui Tao
5.1
Introduction
.............................................. 63
5.2
Related Work
............................................. 64
5.3
Architecture
.............................................. 65
5.4
Background Definitions
.................................... 66
5.4.1
World Knowledge Ontology
........................ 66
5.4.2
Local Instance Repository
.......................... 67
5.5
Specifying Knowledge in an Ontology
........................ 68
5.6
Discovery of Useful Knowledge in LIRs
...................... 70
5.7
Experiments
.............................................. 71
5.7.1
Experiment Design
................................ 71
5.7.2
Other Experiment Settings
.......................... 74
5.8
Results and Discussions
.................................... 75
5.9
Conclusions
.............................................. 77
References
..................................................... 77
Part II Novel KDD Domains
&
Techniques
6
Data Mining Applications in Social Security
..................... 81
Yanchang Zhao, Huaifeng Zhang, Longbing
Cao,
Hans
Bohlscheid,
Yuming
Ou,
and Chengqi Zhang
6.1
Introduction and Background
............................... 81
6.2
Case Study I: Discovering Debtor Demographic Patterns with
Decision Tree and Association Rules
......................... 83
6.2.1
Business Problem and Data
......................... 83
6.2.2
Discovering Demographic Patterns of Debtors
......... 83
6.3
Case Study II: Sequential Pattern Mining to Find Activity
Sequences of Debt Occurrence
.............................. 85
6.3.1
Impact-Targeted Activity Sequences
.................. 86
6.3.2
Experimental Results
.............................. 87
6.4
Case Study III: Combining Association Rules from
Heterogeneous Data Sources to Discover Repayment Patterns
.... 89
6.4.1
Business Problem and Data
......................... 89
6.4.2
Mining Combined Association Rules
................. 89
6.4.3
Experimental Results
.............................. 90
6.5
Case Study IV: Using Clustering and Analysis of Variance to
Verify the Effectiveness of a New Policy
...................... 92
6.5.1
Clustering Declarations with Contour and Clustering
----- 92
6.5.2
Analysis of Variance
............................... 94
6.6
Conclusions and Discussion
................................ 94
References
..................................................... 95
x
Contents
7
Security Data Mining: A Survey Introducing Tamper-Resistance
... 97
Clifton Phua and Mafruz Ashrafi
7.1
Introduction
.............................................. 97
7.2
Security Data Mining
...................................... 98
7.2.1
Definitions
....................................... 98
7.2.2
Specific Issues
.................................... 99
7.2.3
General Issues
....................................101
7.3
Tamper-Resistance
........................................102
7.3.1
Reliable Data
.....................................102
7.3.2
Anomaly Detection Algorithms
......................104
7.3.3
Privacy and Confidentiality Preserving Results
.........105
7.4
Conclusion
...............................................108
References
.....................................................108
8
A Domain Driven Mining Algorithm on Gene Sequence Clustering ..111
Yun Xiong, Ming Chen, and Yangyong Zhu
8.1
Introduction
..............................................
Ill
8.2
Related Work
.............................................112
8.3
The Similarity Based on Biological Domain Knowledge
.........114
8.4
Problem Statement
........................................114
8.5
A Domain-Driven Gene Sequence Clustering Algorithm
........117
8.6
Experiments and Performance Study
.........................121
8.7
Conclusion and Future Work
................................124
References
.....................................................125
9
Domain Driven Tree Mining of Semi-structured Mental Health
Information
................................................ 127
Maja
Hadzic, Fedja Hadzic, and Tharam S. Dillon
9.1
Introduction
..............................................127
9.2
Information Use and Management within Mental Health Domain
. 128
9.3
Tree Mining
-
General Considerations
........................130
9.4
Basic Tree Mining Concepts
................................131
9.5
Tree Mining of Medical Data
...............................135
9.6
Illustration of the Approach
.................................139
9.7
Conclusion and Future Work
................................139
References
.....................................................140
10
Text Mining for Real-time Ontology Evolution
................... 143
Jackei H.K. Wong, Tharam S. Dillon, Allan K.Y. Wong, and Wilfred
W.K. Lin
10.1
Introduction
..............................................144
10.2
Related Text Mining Work
..................................145
10.3
Terminology and Multi-representations
.......................145
10.4
Master Aliases Table and OCOE Data Structures
...............149
10.5
Experimental Results
......................................152
10.5.1
CAV
Construction and Information Ranking
...........153
Contents xj
10.5.2
Real-Time
CAV
Expansion
Supported by Text Mining
.. 154
10.6
Conclusion
...............................................155
10.7
Acknowledgement
........................................156
References
.....................................................156
11
Microarray Data Mining: Selecting Trustworthy Genes with Gene
Feature Ranking
............................................ 159
Franco A. Ubaudi, Paul J. Kennedy, Daniel R. Catchpoole, Dachuan
Guo, and Simeon J. Simoff
11.1
Introduction
..............................................159
11.2
Gene Feature Ranking
.....................................161
11.2.1
Use of Attributes and Data Samples in Gene Feature
Ranking
.........................................162
11.2.2
Gene Feature Ranking: Feature Selection Phase
1.......163
11.2.3
Gene Feature Ranking: Feature Selection Phase
2.......163
11.3
Application of Gene Feature Ranking to Acute Lymphoblastic
Leukemia data
............................................164
11.4
Conclusion
...............................................166
References
.....................................................167
12
Blog Data Mining for Cyber Security Threats
.................... 169
Flora S. Tsai and
Kap Luk Chan
12.1
Introduction
..............................................169
12.2
Review of Related Work
...................................170
12.2.1
Intelligence Analysis
...............................171
12.2.2
Information Extraction from Blogs
...................171
12.3
Probabilistic Techniques for Blog Data Mining
................172
12.3.1
Attributes of Blog Documents
....................... 172
12.3.2
Latent Dirichlet Allocation
.......................... 173
12.3.3
Isometric Feature Mapping
(Isomap)................. 174
12.4
Experiments and Results
................................... 175
12.4.1
Data Corpus
......................................175
12.4.2
Results for Blog Topic Analysis
.....................176
12.4.3
Blog Content Visualization
.........................178
12.4.4
Blog Time Visualization
............................179
12.5
Conclusions
..............................................180
References
.....................................................181
13
Blog Data Mining: The Predictive Power of Sentiments
............ 183
Yang Liu, Xiaohui Yu, Xiangji Huang, and Aijun An
13.1
Introduction
.............................................. 83
13.2
Related Work
.............................................185
13.3
Characteristics of Online Discussions
........................186
13.3.1
Blog Mentions
....................................
1
86
13.3.2
Box Office Data and User Rating
....................
1
87
13.3.3
Discussion
.......................................187
xii
Contents
13.4
S-PLSA: A Probabilistic Approach to Sentiment Mining
........188
13.4.1
Feature Selection
..................................188
13.4.2
Sentiment PLSA
..................................188
13.5
ARSA:
A Sentiment-Aware Model
...........................189
13.5.1
The
Autoregressive
Model
..........................190
13.5.2
Incorporating Sentiments
...........................191
13.6
Experiments
..............................................192
13.6.1
Experiment Settings
...............................192
13.6.2
Parameter Selection
................................193
13.7
Conclusions and Future Work
...............................194
References
.....................................................194
14
Web Mining: Extracting Knowledge from the World Wide Web
___197
Zhongzhi Shi, Huifang Ma, and Qing He
14.1
Overview of Web Mining Techniques
........................197
14.2
Web Content Mining
......................................199
14.2.1
Classification: Multi-hierarchy Text Classification
......199
14.2.2
Clustering Analysis: Clustering Algorithm Based on
Swarm Intelligence and k-Means
....................200
14.2.3
Semantic Text Analysis: Conceptual Semantic Space
.... 202
14.3
Web Structure Mining: PageRank vs. HITS
...................203
14.4
Web Event Mining
........................................204
14.4.1
Preprocessing for Web Event Mining
.................205
14.4.2
Multi-document Summarization: A Way to
Demonstrate Event s Cause and Effect
................206
14.5
Conclusions and Future Works
..............................206
References
.....................................................207
15
DAG Mining for Code Compaction
.............................209
T. Werth, M.
Wörlein,
A. Dreweke, I. Fischer, and M. Philippsen
15.1
Introduction
..............................................209
15.2
Related Work
.............................................211
15.3
Graph and DAG Mining Basics
..............................211
15.3.1
Graph-based versus Embedding-based Mining
........212
15.3.2
Embedded versus Induced Fragments
.................213
15.3.3
DAG Mining Is W-complete
.......................213
15.4
Algorithmic Details of DAGMA
.............................214
15.4.1
A Canonical Form for DAG enumeration
..............214
15.4.2
Basic Structure of the DAG Mining Algorithm
.........215
15.4.3
Expansion Rules
..................................216
15.4.4
Application to Procedural Abstraction
................219
15.5
Evaluation
...............................................220
15.6
Conclusion and Future Work
................................222
References
.....................................................223
Contents
x¡¡¡
16
A Framework for Context-Aware Trajectory Data Mining
.........225
Vania Bogorny and Monica Wachowicz
16.1
Introduction
..............................................225
16.2
Basic Concepts
...........................................227
16.3
A Domain-driven Framework for Trajectory Data Mining
........229
16.4
Case Study
...............................................232
16.4.1
The Selected Mobile Movement-aware Outdoor Game
.. 233
16.4.2
Transportation Application
..........................234
16.5
Conclusions and Future Trends
..............................238
References
.....................................................239
17
Census Data Mining for Land Use Classification
.................241
E.
Roma Neto
and D. S. Hamburger
17.1
Content Structure
.........................................241
17.2
Key Research Issues
.......................................242
17.3
Land Use and Remote Sensing
..............................242
] 7.4
Census Data and Land Use Distribution
.......................243
17.5
Census Data Warehouse and Spatial Data Mining
..............243
17.5.1
Concerning about Data Quality
.....................243
17.5.2
Concerning about Domain Driven
....................244
17.5.3
Applying Machine Learning Tools
...................246
17.6
Data Integration
..........................................247
17.6.1
Area of Study and Data
.............................247
17.6.2
Supported Digital Image Processing
..................248
17.6.3
Putting All Steps Together
..........................248
17.7
Results and Analysis
......................................249
References
.....................................................251
18
Visual Data Mining for Developing Competitive Strategies in
Higher Education
...........................................253
Giirdal
Ertek
18.1
Introduction
..............................................253
18.2
Square Tiles Visualization
..................................255
18.3
Related Work
.............................................256
18.4
Mathematical Model
.......................................257
18.5
Framework and Case Study
.................................260
18.5.1
General Insights and Observations
...................261
18.5.2
Benchmarking
....................................262
18.5.3
High School Relationship Management (HSRM)
.......263
18.6
Future Work
..............................................264
18.7
Conclusions
..............................................264
References
.....................................................265
xiv Contents
19 Data Mining
For
Robust
Flight Scheduling......................
267
Ira Assent, Ralph
Krieger, Petra
Welter,
Jörg Herbers, and Thomas
Seidl
19.1
Introduction
..............................................267
19.2
Flight Scheduling in the Presence of Delays
...................268
19.3
Related Work
.............................................270
19.4
Classification of Flights
....................................272
19.4.1
Subspaces for Locally Varying Relevance
.............272
19.4.2
Integrating Subspace Information for Robust Flight
Classification
.....................................272
19.5
Algorithmic Concept
......................................274
19.5.1
Monotonicity
Properties of Relevant Attribute Subspaces
274
19.5.2
Top-down Class Entropy Algorithm: Lossless Pruning
Theorem
.........................................275
19.5.3
Algorithm: Subspaces, Clusters, Subspace Classification
. 276
19.6
Evaluation of Flight Delay Classification in Practice
............278
19.7
Conclusion
...............................................280
References
.....................................................280
20
Data Mining for Algorithmic Asset Management
.................283
Giovanni Montana and Francesco Parrella
20.1
Introduction
..............................................283
20.2
Backbone of the Asset Management System
...................285
20.3
Expert-based Incremental Learning
..........................286
20.4
An Application to the ¡Share Index Fund
......................290
References
.....................................................294
Reviewer List
......................................................297
Index
.............................................................299
|
| any_adam_object | 1 |
| building | Verbundindex |
| bvnumber | BV035334463 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.9.D343 |
| callnumber-search | QA76.9.D343 |
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| callnumber-subject | QA - Mathematics |
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| ctrlnum | (OCoLC)244765613 (DE-599)DNB989384659 |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV035334463 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:31:31Z |
| institution | BVB |
| isbn | 9780387794198 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017138853 |
| oclc_num | 244765613 |
| open_access_boolean | |
| owner | DE-703 DE-91G DE-BY-TUM DE-1050 DE-2070s |
| owner_facet | DE-703 DE-91G DE-BY-TUM DE-1050 DE-2070s |
| physical | XIX, 302 S. Ill., graph. Darst., Kt. 235 mm x 155 mm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Springer |
| record_format | marc |
| spelling | Data mining for business applications ed. by Longbing Cao ... New York Springer 2009 XIX, 302 S. Ill., graph. Darst., Kt. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier 'Data Mining for Business Applications' presents the state-of-the-art research & development outcomes on methodologies, techniques, approaches & successful applications in the area. Datenverarbeitung Wirtschaft Business Data processing Data mining Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Unternehmen (DE-588)4061963-1 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Data Mining (DE-588)4428654-5 s DE-604 Unternehmen (DE-588)4061963-1 s Künstliche Intelligenz (DE-588)4033447-8 s Datenverarbeitung (DE-588)4011152-0 s Cao, Longbing Sonstige oth Erscheint auch als Online-Ausgabe 978-0-387-79420-4 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017138853&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Data mining for business applications Datenverarbeitung Wirtschaft Business Data processing Data mining Datenverarbeitung (DE-588)4011152-0 gnd Unternehmen (DE-588)4061963-1 gnd Data Mining (DE-588)4428654-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4061963-1 (DE-588)4428654-5 (DE-588)4033447-8 (DE-588)4143413-4 |
| title | Data mining for business applications |
| title_auth | Data mining for business applications |
| title_exact_search | Data mining for business applications |
| title_full | Data mining for business applications ed. by Longbing Cao ... |
| title_fullStr | Data mining for business applications ed. by Longbing Cao ... |
| title_full_unstemmed | Data mining for business applications ed. by Longbing Cao ... |
| title_short | Data mining for business applications |
| title_sort | data mining for business applications |
| topic | Datenverarbeitung Wirtschaft Business Data processing Data mining Datenverarbeitung (DE-588)4011152-0 gnd Unternehmen (DE-588)4061963-1 gnd Data Mining (DE-588)4428654-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Datenverarbeitung Wirtschaft Business Data processing Data mining Unternehmen Data Mining Künstliche Intelligenz Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017138853&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT caolongbing dataminingforbusinessapplications |