Design and implementation of data mining tools:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2009
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIII, 250 S. Ill., graph. Darst. |
| ISBN: | 9781420045901 |
Internformat
MARC
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| 008 | 071010s2009 xx ad|| |||| 00||| eng d | ||
| 020 | |a 9781420045901 |9 978-1-4200-4590-1 | ||
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| 245 | 1 | 0 | |a Design and implementation of data mining tools |c M. Awad ... |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2009 | |
| 300 | |a XXIII, 250 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 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 Awad, Mamoun |e Sonstige |4 oth | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016080747&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-016080747 | |
Datensatz im Suchindex
| _version_ | 1823676285542465536 |
|---|---|
| adam_text |
Contents
Preface
.
,
.xv
About the Authors
.xxi
Acknowledgments
.xxiii
Chapter
1
Introduction
.1
1.1
Trends
.1
1.2
Data Mining Techniques and Applications
.2
1.3
Data Mining for Cyber Security: Intrusion Detection
.2
1.4
Data Mining for Web: Web Page Surfing Prediction
.3
1.5
Data Mining for Multimedia: Image Classification
.4
1.6
Organization of This Book
.5
1.7
Next Steps
.5
PART I DATA MINING TECHNIQUES
AND APPLICATIONS
Introduction to Part 1
.9
Chapter
2
Data Mining Techniques
.
11
2.1
Introduction
.'
2.2
Overview of Data Mining Tasks and Techniques
.12
2.3
Artificial Neural Networks
.13
2.4
Support Vector Machines
.
^
2.5
Markov Model
.
19
2.6
Association Rule Mining (ARM)
.22
2.7
Muhiclass Problem
.25
2.7.1
One-vs-One
.
25
2.7.2
One-vs-All
.
2б
VII
viii
■ Contents
2.8 Image Mining.26
2.8.1 Feature
Selection
.27
2.8.2 Automatic Image Annotation.28
2.8.3 Image
Classification
.28
2.9
Summary.
29
References
.29
Chapter
3
Data Mining Applications
.31
3.1
Introduction
.31
3.2
Intrusion Detection
.33
3.3
Web Page Surfing Prediction
.35
3.4
Image Classification
.37
3.5
Summary
.38
References
.38
Conclusion to Part 1
.41
PART II DATA MINING TOOL FOR INTRUSION DETECTION
Introduction to Part II
.43
Chapter
4
Data Mining for Security Applications
.45
4.1
Overview
.45
4.2
Data Mining for Cyber Security
.46
4.2.1
Overview
.46
4.2.2
Cyber Terrorism, Insider Threats, and External Attacks
.
A7
4.2.3
Malicious Intrusions
.48
4.2.4
Credit Card Fraud and Identity Theft
.48
4.2.5
Attacks on Critical Infrastructures
.49
4.2.6
Data Mining for Cyber Security
.49
4.3
Current Research and Development
.51
4.4
Summary and Directions
.53
References
.53
Chapter
5
Dynamic Growing Self-Organizing Tree Algorithm
.55
5.1
Overview
.55
5.2
Our Approach
.56
5.3
DGSOT
.58
5.3.1
Vertical Growing
.58
5.3.2
Learning Process
.59
Contents ■ ix
5.3.3 Horizontal
Growing.
61
5.3.4
Stopping Rule for Horizontal Growing
.61
5.3.5
A'-Level Up Distribution (KLD)
.62
5.4
Discussion
.63
5.5
Summary and Directions
.63
References
.64
Chapter
6
Data Reduction Using Hierarchical
Clustering and
Rocchio
Bundling
.65
6.1
Overview
.65
6.2
Our Approach
.66
6.2.1
Enhancing the Training Process of SVM
.66
6.2.2
Stopping Criteria
.67
6.3
Complexity and Analysis
.69
6.4
Rocchio
Decision Boundary
.73
6.5
Rocchio
Bundling Technique
.74
6.6
Summary and Directions
.74
References
.75
Chapter
7
Intrusion Detection Results
.77
7.1
Overview
.77
7.2
Dataset
.78
7.3
Results
.78
7.4
Complexity Validation
.80
7.5
Discussion
.81
7.6
Summary and Directions
.82
References
.82
Conclusion to Part II
.82
PART III DATA MINING TOOL FOR WEB PAGE
SURFING PREDICTION
Introduction to Part III
.83
Chapter
8
Web Data Management and Mining
.85
8.1
Overview
.85
8.2
Digital Libraries
.86
8.2.1
Overview
.86
8.2.2
Web Database Management
.87
Contents
8.2.3
Search Engines
.88
8.2.4
Question-Answering Systems
.90
8.3
E-Commerce Technologies
.90
8.4
Semantic Web Technologies
.92
8.5
Web Data Mining
.94
8.6
Summary and Directions
.95
References
.95
Chapter
9
Effective Web Page Prediction Using Hybrid Model
.97
9.1
Overview
.97
9.2
Our Approach
.98
9.3
Feature Extraction
.99
9.4
Domain Knowledge and Classifier Reduction
.100
9.5
Summary
.101
References
.101
Chapter
10
Multiple Evidence Combination for WWW Prediction
.103
10.1
Overview
.103
10.2
Fitting a Sigmoid after SVM
.104
10.3
Fittinga
Sigmoid after ANN Output
.106
10.4
Dempster—Shafer for Evidence Combination
.107
10.5
Dempster's Rule for Evidence Combination
.108
10.6
Using Dempster-Shafer Theory in WWW Prediction
.110
10.7
Summary and Directions
.113
References
.113
Chapter
11
WWW Prediction Results
.115
11.1
Overview
.115
11.2
Terminology
.115
11.3
Data Processing
.117
11.4
Experiment Setup
.117
11.5
Results
.119
11.6
Discussion
.128
11.7
Summary and Directions
.128
References
.129
Conclusion to Part III
.129
Contents ■ xi
PART IV
DATA MINING TOOL
FOR
IMAGE
CLASSIFICATION
Introduction to Part IV
.131
Chapter
12
Multimedia Data Management and Mining
.133
12.1
Overview
.133
12.2
Managing and Mining Multimedia Data
.134
12.3
Management and Mining Text, Image, Audio, and Video Data.
135
12.3.1
Text Retrieval
.135
12.3.2
Image Retrieval
.136
12.3.3
Video Retrieval
.137
12.3.4
Audio Retrieval
.138
12.4
Summary and Directions
.139
References
.139
Chapter
13
Image Classification Models
.141
13.1
Overview
.141
13.2
Example Models
.142
13.2.1
Statistical Models for Image Annotation
.142
13.2.2
Co-Occurrence Model for Image Annotation
.142
13.2.3
Translation Model
.143
13.2.4
Cross-Media Relevance Model (CMRM)
.144
13.2.5
Continuous Relevance Model
.145
13.2.6
Other Models
.146
13.3
Image Classification
.146
13.3.1
Dimensionality Reduction
.147
13.3.2
Feature Transformation
.147
13.3.3
Feature Selection
.147
13.3.4
Subspace Clustering Algorithms
.148
13.4
Summary
.150
References
.150
Chapter
14
Subspace Clustering and Automatic Image Annotation
.153
14.1
Introduction
.153
14.2
Proposed Automatic Image Annotation Framework
.154
14.2.1
Segmentation
.155
xii ■ Contents
14.3 The
Vector
Space Model
.157
14.3.1
Blob Tokens: Keywords of Visual Language
.157
14.3.2
Probability Table
.157
14.4
Clustering Algorithm for Blob Token Generation
.158
14.4.1
K-Means
.158
14.4.2
Fuzzy K-Means Algorithm
.159
14.4.3
Weighted Feature Selection Algorithm
.160
14.5
Construction of the Probability Table
.164
14.5.1
Method
1:
Unweighted Data Matrix
.164
14.5.2
Method
2:
tPidfWeighted Data Matrix
.164
14.5.3
Method
3:
Singular Value Decomposition
(SVD)
.165
14.5.4
Method
4:
EM Algorithm
.166
14.5.5
Fuzzy Method
.167
14.6 AutoAnnotation.168
14.7
Experimental Setup
.168
14.7.1
Corel
Dataset
.168
14.7.2
Feature Description
.170
14.8
Evaluation Methods
.170
14.8.1
Evaluation of Annotation
.170
14.8.2
Evaluation of Correspondence
.171
14.9
Results
.171
14.9.1
Results of Fuzzy Method
.176
14.9.2
Discussion
.176
14.10
Summary
.177
References
.177
Chapter
15
Enhanced Weighted Feature Selection
.179
15.1
Introduction
.179
15.2 Aggressive Feature Weighting Algorithm
.180
15.2.1
Global Data Reduction (GDR)
.180
15.2.2
Weighted Feature Using Chi-Square
.181
15.2.3
Linear Discriminant Analysis
.182
1 5.2.4
Link between Keyword and Blob Token
.185
15.2.4.1
Correlation Method (CRM)
.185
15.2.4.2
Cosine Method (CSM)
.185
15-2.4.3
Conservative Context (C2)
.186
15.3
Experiment Results
.187
15.3.1
Results of LDA
.192
15.4
Summary and Directions
.193
References
.193
Contents ■ xiii
Chapter
16
Image
Classification and Performance Analysis
.195
16.1
Introduction
.195
16.2
Classifiers
.196
16.2.1
./^-Nearest Neighbor Algorithm
.196
16.2.2
Distance Weighted KNN (DWKNN)
.196
16.2.3
Fuzzy KNN
.197
16.2.4
Nearest Prototype Classifier (NPC)
.198
16.3
Evidence Theory and KNN
.198
16.3.1
Dempster-Shafer Evidence Theory
.198
16.3.2
Evidence-Theory-Based KNN (EKNN)
.199
16.3.3
Density-Based EKNN (DEKNN)
.202
16.4
Experiment Results
.203
16.4.1
ImageCLEFmed
2006
Dataset
.203
16.4.2
Imbalanced Data Problem
.203
16.4.3
Results
.206
16.6
Discussion
.212
16.6.1
Enhancement: Spatial Association Rule Mining
.212
16.6.2
WordNet and Semantic Similarity
.213
16.6.3
Domain Knowledge
.215
16.7
Summary and Directions
.215
References
.215
Chapter
17
Summary and Directions
.217
17.1
Overview
.217
17.2
Summary of This Book
.217
17.3
Directions for Data Mining Tools
.220
17.4
Where Do We Go from Here?
.222
Conclusion to Part IV
.223
APPENDIX A
.225
Data Management Systems: Developments and Trends
.227
A.I Overview
.227
A.
2
Developments in Database Systems
.228
A.3 Status, Vision, and Issues
.232
A.4 Data Management Systems Framework
.233
A.
5
Building Information Systems from the Framework
.236
A.6 Relationships among the Texts
.239
A.7 Summary and Directions
.241
References
.241
Index
.243 |
| adam_txt |
Contents
Preface
.
,
.xv
About the Authors
.xxi
Acknowledgments
.xxiii
Chapter
1
Introduction
.1
1.1
Trends
.1
1.2
Data Mining Techniques and Applications
.2
1.3
Data Mining for Cyber Security: Intrusion Detection
.2
1.4
Data Mining for Web: Web Page Surfing Prediction
.3
1.5
Data Mining for Multimedia: Image Classification
.4
1.6
Organization of This Book
.5
1.7
Next Steps
.5
PART I DATA MINING TECHNIQUES
AND APPLICATIONS
Introduction to Part 1
.9
Chapter
2
Data Mining Techniques
.
11
2.1
Introduction
.'
2.2
Overview of Data Mining Tasks and Techniques
.12
2.3
Artificial Neural Networks
.13
2.4
Support Vector Machines
.
^
2.5
Markov Model
.
19
2.6
Association Rule Mining (ARM)
.22
2.7
Muhiclass Problem
.25
2.7.1
One-vs-One
.
25
2.7.2
One-vs-All
.
2б
VII
viii
■ Contents
2.8 Image Mining.26
2.8.1 Feature
Selection
.27
2.8.2 Automatic Image Annotation.28
2.8.3 Image
Classification
.28
2.9
Summary.
29
References
.29
Chapter
3
Data Mining Applications
.31
3.1
Introduction
.31
3.2
Intrusion Detection
.33
3.3
Web Page Surfing Prediction
.35
3.4
Image Classification
.37
3.5
Summary
.38
References
.38
Conclusion to Part 1
.41
PART II DATA MINING TOOL FOR INTRUSION DETECTION
Introduction to Part II
.43
Chapter
4
Data Mining for Security Applications
.45
4.1
Overview
.45
4.2
Data Mining for Cyber Security
.46
4.2.1
Overview
.46
4.2.2
Cyber Terrorism, Insider Threats, and External Attacks
.
A7
4.2.3
Malicious Intrusions
.48
4.2.4
Credit Card Fraud and Identity Theft
.48
4.2.5
Attacks on Critical Infrastructures
.49
4.2.6
Data Mining for Cyber Security
.49
4.3
Current Research and Development
.51
4.4
Summary and Directions
.53
References
.53
Chapter
5
Dynamic Growing Self-Organizing Tree Algorithm
.55
5.1
Overview
.55
5.2
Our Approach
.56
5.3
DGSOT
.58
5.3.1
Vertical Growing
.58
5.3.2
Learning Process
.59
Contents ■ ix
5.3.3 Horizontal
Growing.
61
5.3.4
Stopping Rule for Horizontal Growing
.61
5.3.5
A'-Level Up Distribution (KLD)
.62
5.4
Discussion
.63
5.5
Summary and Directions
.63
References
.64
Chapter
6
Data Reduction Using Hierarchical
Clustering and
Rocchio
Bundling
.65
6.1
Overview
.65
6.2
Our Approach
.66
6.2.1
Enhancing the Training Process of SVM
.66
6.2.2
Stopping Criteria
.67
6.3
Complexity and Analysis
.69
6.4
Rocchio
Decision Boundary
.73
6.5
Rocchio
Bundling Technique
.74
6.6
Summary and Directions
.74
References
.75
Chapter
7
Intrusion Detection Results
.77
7.1
Overview
.77
7.2
Dataset
.78
7.3
Results
.78
7.4
Complexity Validation
.80
7.5
Discussion
.81
7.6
Summary and Directions
.82
References
.82
Conclusion to Part II
.82
PART III DATA MINING TOOL FOR WEB PAGE
SURFING PREDICTION
Introduction to Part III
.83
Chapter
8
Web Data Management and Mining
.85
8.1
Overview
.85
8.2
Digital Libraries
.86
8.2.1
Overview
.86
8.2.2
Web Database Management
.87
Contents
8.2.3
Search Engines
.88
8.2.4
Question-Answering Systems
.90
8.3
E-Commerce Technologies
.90
8.4
Semantic Web Technologies
.92
8.5
Web Data Mining
.94
8.6
Summary and Directions
.95
References
.95
Chapter
9
Effective Web Page Prediction Using Hybrid Model
.97
9.1
Overview
.97
9.2
Our Approach
.98
9.3
Feature Extraction
.99
9.4
Domain Knowledge and Classifier Reduction
.100
9.5
Summary
.101
References
.101
Chapter
10
Multiple Evidence Combination for WWW Prediction
.103
10.1
Overview
.103
10.2
Fitting a Sigmoid after SVM
.104
10.3
Fittinga
Sigmoid after ANN Output
.106
10.4
Dempster—Shafer for Evidence Combination
.107
10.5
Dempster's Rule for Evidence Combination
.108
10.6
Using Dempster-Shafer Theory in WWW Prediction
.110
10.7
Summary and Directions
.113
References
.113
Chapter
11
WWW Prediction Results
.115
11.1
Overview
.115
11.2
Terminology
.115
11.3
Data Processing
.117
11.4
Experiment Setup
.117
11.5
Results
.119
11.6
Discussion
.128
11.7
Summary and Directions
.128
References
.129
Conclusion to Part III
.129
Contents ■ xi
PART IV
DATA MINING TOOL
FOR
IMAGE
CLASSIFICATION
Introduction to Part IV
.131
Chapter
12
Multimedia Data Management and Mining
.133
12.1
Overview
.133
12.2
Managing and Mining Multimedia Data
.134
12.3
Management and Mining Text, Image, Audio, and Video Data.
135
12.3.1
Text Retrieval
.135
12.3.2
Image Retrieval
.136
12.3.3
Video Retrieval
.137
12.3.4
Audio Retrieval
.138
12.4
Summary and Directions
.139
References
.139
Chapter
13
Image Classification Models
.141
13.1
Overview
.141
13.2
Example Models
.142
13.2.1
Statistical Models for Image Annotation
.142
13.2.2
Co-Occurrence Model for Image Annotation
.142
13.2.3
Translation Model
.143
13.2.4
Cross-Media Relevance Model (CMRM)
.144
13.2.5
Continuous Relevance Model
.145
13.2.6
Other Models
.146
13.3
Image Classification
.146
13.3.1
Dimensionality Reduction
.147
13.3.2
Feature Transformation
.147
13.3.3
Feature Selection
.147
13.3.4
Subspace Clustering Algorithms
.148
13.4
Summary
.150
References
.150
Chapter
14
Subspace Clustering and Automatic Image Annotation
.153
14.1
Introduction
.153
14.2
Proposed Automatic Image Annotation Framework
.154
14.2.1
Segmentation
.155
xii ■ Contents
14.3 The
Vector
Space Model
.157
14.3.1
Blob Tokens: Keywords of Visual Language
.157
14.3.2
Probability Table
.157
14.4
Clustering Algorithm for Blob Token Generation
.158
14.4.1
K-Means
.158
14.4.2
Fuzzy K-Means Algorithm
.159
14.4.3
Weighted Feature Selection Algorithm
.160
14.5
Construction of the Probability Table
.164
14.5.1
Method
1:
Unweighted Data Matrix
.164
14.5.2
Method
2:
tPidfWeighted Data Matrix
.164
14.5.3
Method
3:
Singular Value Decomposition
(SVD)
.165
14.5.4
Method
4:
EM Algorithm
.166
14.5.5
Fuzzy Method
.167
14.6 AutoAnnotation.168
14.7
Experimental Setup
.168
14.7.1
Corel
Dataset
.168
14.7.2
Feature Description
.170
14.8
Evaluation Methods
.170
14.8.1
Evaluation of Annotation
.170
14.8.2
Evaluation of Correspondence
.171
14.9
Results
.171
14.9.1
Results of Fuzzy Method
.176
14.9.2
Discussion
.176
14.10
Summary
.177
References
.177
Chapter
15
Enhanced Weighted Feature Selection
.179
15.1
Introduction
.179
15.2 Aggressive Feature Weighting Algorithm
.180
15.2.1
Global Data Reduction (GDR)
.180
15.2.2
Weighted Feature Using Chi-Square
.181
15.2.3
Linear Discriminant Analysis
.182
1 5.2.4
Link between Keyword and Blob Token
.185
15.2.4.1
Correlation Method (CRM)
.185
15.2.4.2
Cosine Method (CSM)
.185
15-2.4.3
Conservative Context (C2)
.186
15.3
Experiment Results
.187
15.3.1
Results of LDA
.192
15.4
Summary and Directions
.193
References
.193
Contents ■ xiii
Chapter
16
Image
Classification and Performance Analysis
.195
16.1
Introduction
.195
16.2
Classifiers
.196
16.2.1
./^-Nearest Neighbor Algorithm
.196
16.2.2
Distance Weighted KNN (DWKNN)
.196
16.2.3
Fuzzy KNN
.197
16.2.4
Nearest Prototype Classifier (NPC)
.198
16.3
Evidence Theory and KNN
.198
16.3.1
Dempster-Shafer Evidence Theory
.198
16.3.2
Evidence-Theory-Based KNN (EKNN)
.199
16.3.3
Density-Based EKNN (DEKNN)
.202
16.4
Experiment Results
.203
16.4.1
ImageCLEFmed
2006
Dataset
.203
16.4.2
Imbalanced Data Problem
.203
16.4.3
Results
.206
16.6
Discussion
.212
16.6.1
Enhancement: Spatial Association Rule Mining
.212
16.6.2
WordNet and Semantic Similarity
.213
16.6.3
Domain Knowledge
.215
16.7
Summary and Directions
.215
References
.215
Chapter
17
Summary and Directions
.217
17.1
Overview
.217
17.2
Summary of This Book
.217
17.3
Directions for Data Mining Tools
.220
17.4
Where Do We Go from Here?
.222
Conclusion to Part IV
.223
APPENDIX A
.225
Data Management Systems: Developments and Trends
.227
A.I Overview
.227
A.
2
Developments in Database Systems
.228
A.3 Status, Vision, and Issues
.232
A.4 Data Management Systems Framework
.233
A.
5
Building Information Systems from the Framework
.236
A.6 Relationships among the Texts
.239
A.7 Summary and Directions
.241
References
.241
Index
.243 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| building | Verbundindex |
| bvnumber | BV022875703 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.9.D3 |
| callnumber-search | QA76.9.D3 |
| callnumber-sort | QA 276.9 D3 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 530 |
| ctrlnum | (OCoLC)255443827 (DE-599)BVBBV022875703 |
| dewey-full | 005.74 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.74 |
| dewey-search | 005.74 |
| dewey-sort | 15.74 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| id | DE-604.BV022875703 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:48:54Z |
| indexdate | 2025-02-10T13:13:53Z |
| institution | BVB |
| isbn | 9781420045901 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016080747 |
| oclc_num | 255443827 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG |
| owner_facet | DE-473 DE-BY-UBG |
| physical | XXIII, 250 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Design and implementation of data mining tools M. Awad ... Boca Raton, Fla. [u.a.] CRC Press 2009 XXIII, 250 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Data mining Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Awad, Mamoun Sonstige oth Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016080747&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Design and implementation of data mining tools Data mining Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Design and implementation of data mining tools |
| title_auth | Design and implementation of data mining tools |
| title_exact_search | Design and implementation of data mining tools |
| title_exact_search_txtP | Design and implementation of data mining tools |
| title_full | Design and implementation of data mining tools M. Awad ... |
| title_fullStr | Design and implementation of data mining tools M. Awad ... |
| title_full_unstemmed | Design and implementation of data mining tools M. Awad ... |
| title_short | Design and implementation of data mining tools |
| title_sort | design and implementation of data mining tools |
| topic | Data mining Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data mining Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016080747&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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