Scientific data mining: a practical perspective
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Philadelphia
Society for Industrial and Applied Mathematics
2009
|
| Series: | Other titles in applied mathematics
112 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XVIII, 286 S. Ill., graph. Darst. |
| ISBN: | 9780898716757 |
Staff View
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Record in the Search Index
| _version_ | 1804138890949296128 |
|---|---|
| adam_text | Titel: Scientific data mining
Autor: Kamath, Chandrika
Jahr: 2009
Contents
Preface xiii
1 Introduction 1
1.1 Defining data mining 1
1.2 Mining science and engineering data 2
1.3 Summary 3
2 Data Mining in Science and Engineering 5
2.1 Astronomy and astrophysics 6
2.1.1 Characteristics of astronomy data 10
2.2 Remote sensing 12
2.2.1 Characteristics of remote sensing data 15
2.3 Biological sciences 17
2.3.1 Bioinformatics 18
2.3.2 Medicine 19
2.3.3 Characteristics of biological data 21
2.4 Security and surveillance 21
2.4.1 Biometrics 22
2.4.2 Surveillance 22
2.4.3 Network intrusion detection 23
2.4.4 Automated target recognition 24
2.4.5 Characteristics of security and surveillance data 24
2.5 Computer simulations 25
2.5.1 Characteristics of simulation data 29
2.6 Experimental physics 30
2.6.1 Characteristics of experimental physics data 34
2.7 Information retrieval 34
2.7. i Characteristics of retrieval problems 37
2.8 Other applications 37
2.8.1 Nondestructive testing 37
2.8.2 Earth, environmental, and atmospheric sciences 37
2.8.3 Chemistry and cheminformaties 3K
2.8.4 Materials science and materials informatics 38
2.8.5 Manufacturing 38
2.8.6 Scientific and information visualization 38
vii
viii Contents
2.9 Summary 39
2.10 Suggestions for further reading 39
3 Common Themes in Mining Scientific Data 41
3.1 Types of scientific data 41
3.1.1 Table data 42
3.1.2 Image data 42
3.1.3 Mesh data 43
3.2 Characteristics of scientific data 46
3.2.1 Multispectral, multisensor, multimodal data 46
3.2.2 Spatiotemporal data 46
3.2.3 Compressed data 47
3.2.4 Streaming data 47
3.2.5 Massive data 47
3.2.6 Distributed data 48
3.2.7 Different data formats 48
3.2.8 Different output schemes 49
3.2.9 Noisy, missing, and uncertain data 49
3.2.10 Low-level data, higher-level objects 50
3.2.11 Representation of objects in the data 51
3.2.12 High-dimensional data 52
3.2.13 Size and quality of labeled data 52
3.3 Characteristics of scientific data analysis 53
3.4 Summary 55
3.5 Suggestions for further reading 56
4 The Scientific Data Mining Process 57
4.1 The tasks in the scientific data mining process 57
4.1.1 Transforming raw data into target data 58
4.1.2 Transforming target data into preprocessed data 60
4.1.3 Converting preprocessed data into transformed data ... 62
4.1.4 Converting transformed data into patterns 63
4.1.5 Converting patterns into knowledge 63
4.2 General observations about the scientific data mining process 64
4.3 Defining scientific data mining: The rationale 65
4.4 Summary 66
5 Reducing the Size of the Data 67
5.1 Sampling 67
5.2 Multiresolution techniques 70
5.3 Summary 76
5.4 Suggestions for further reading 77
6 Fusing Different Data Modalities 79
6.1 The need for data fusion 80
6.2 Levels of data fusion 81
6.3 Sensor-level data fusion 83
6.3.1 Multiple target tracking 84
Contents ix
6.3.2 Image registration 85
6.4 Feature-level data fusion 90
6.5 Decision-level data fusion 91
6.6 Summary 92
6.7 Suggestions for further reading 92
7 Enhancing Image Data 93
7.1 The need for image enhancement 94
7.2 Image denoising 95
7.2.1 Filter-based approaches 95
7.2.2 Wavelet-based approaches 99
7.2.3 Partial differential equation-based approaches 102
7.2.4 Removing multiplicative noise 105
7.2.5 Problem-specific denoising 106
7.3 Contrast enhancement 107
7.4 Morphological techniques 110
7.5 Summary Ill
7.6 Suggestions for further reading Ill
8 Finding Objects in the Data 113
8.1 Edge-based techniques 114
8.1.1 The Canny edge detector 117
8.1.2 Active contours 117
8.1.3 The USAN approach 123
8.2 Region-based methods 123
8.2.1 Region splitting 124
8.2.2 Region merging 124
8.2.3 Region splitting and merging 125
8.2.4 Clustering and classification 127
8.2.5 Watershed segmentation 128
8.3 Salient regions 128
8.3.1 Corners 129
8.3.2 Scale saliency regions 129
8.3.3 Scale-invariant feature transforms 131
8.4 Detecting moving objects 132
8.4.1 Background subtraction 132
8.4.2 Block matching 133
8.5 Domain-specific approaches 135
8.6 Identifying unique objects 136
8.7 Postprocessing for object identification 138
8.8 Representation of the objects 138
8.9 Summary 139
8.10 Suggestions for further reading 139
9 Extracting Features Describing the Objects 141
9.1 General requirements for a feature 142
9.2 Simple features 144
x Contents
9.3 Shape features 146
9.4 Texture features 149
9.5 Problem-specific features 153
9.6 Postprocessing the features 157
9.7 Summary 159
9.8 Suggestions for further reading 160
10 Reducing the Dimension of the Data 161
10.1 The need for dimension reduction 162
10.2 Feature transform methods 164
10.2.1 Principal component analysis 164
10.2.2 Extensions of principal component analysis 166
10.2.3 Random projections 166
10.2.4 Multidimensional scaling 167
10.2.5 FastMap 167
10.2.6 Self-organizing maps 168
10.3 Feature subset selection methods 168
10.3.1 Filters for feature selection 169
10.3.2 Wrapper methods 171
10.3.3 Feature selection for regression 172
10.4 Domain-specific methods 172
10.5 Representation of high-dimensional data 174
10.6 Summary 175
10.7 Suggestions for further reading 175
11 Finding Patterns in the Data 177
11.1 Clustering 178
11.1.1 Partitional algorithms 179
11.1.2 Hierarchical clustering 180
11.1.3 Graph-based clustering 180
11.1.4 Observations and further reading 183
11.2 Classification 184
11.2.1 A:-nearest neighbor classifier 185
11.2.2 Naive Bayes classifier 185
11.2.3 Decision trees 186
11.2.4 Neural networks 189
11.2.5 Support vector machines 193
11.2.6 Ensembles of classifiers 194
11.2.7 Observations and further reading 196
11.3 Regression 199
11.3.1 Statistical techniques 200
11.3.2 Machine learning techniques 200
11.4 Association rules 201
11.5 Tracking 202
11.6 Outlier or anomaly detection 204
11.7 Related topics 205
11.7.1 Distance metrics 205
Contents xi
11.7.2 Optimization techniques 206
11.8 Summary 207
11.9 Suggestions for further reading 207
12 Visualizing the Data and Validating the Results 209
12.1 Visualizing table data 210
12.1.1 Box plots 210
12.1.2 Scatter plots 211
12.1.3 Parallel plots 212
12.2 Visualizing image and mesh data 214
12.3 Validation of results 215
12.4 Summary 218
12.5 Suggestions for further reading 219
13 Scientific Data Mining Systems 221
13.1 Software for specific tasks in scientific data mining 222
13.2 Software systems for scientific data mining 222
13.2.1 Diamond Eye 223
13.2.2 Algorithm Development and Mining System 224
13.2.3 Sapphire 224
13.3 Summary 227
14 Lessons Learned, Challenges, and Opportunities 229
14.1 Guidelines for getting started 230
14.2 Challenges and opportunities 232
14.3 Concluding remarks 233
Bibliography 235
Index 279
|
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| subject_GND | (DE-588)4428654-5 |
| title | Scientific data mining a practical perspective |
| title_auth | Scientific data mining a practical perspective |
| title_exact_search | Scientific data mining a practical perspective |
| title_full | Scientific data mining a practical perspective Chandrika Kamath |
| title_fullStr | Scientific data mining a practical perspective Chandrika Kamath |
| title_full_unstemmed | Scientific data mining a practical perspective Chandrika Kamath |
| title_short | Scientific data mining |
| title_sort | scientific data mining a practical perspective |
| title_sub | a practical perspective |
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