Knowledge Discovery in Spatial Data (eBook)

Yee Leung (Autor)

eBook Download: PDF

2010 | 2009
XXIX, 360 Seiten
Springer Berlin (Verlag)
978-3-642-02664-5 (ISBN)

Infos

When I ?rst came across the term data mining and knowledge discovery in databases, I was excited and curious to ?nd out what it was all about. I was excited because the term tends to convey a new ?eld that is in the making. I was curious because I wondered what it was doing that the other ?elds of research, such as statistics and the broad ?eld of arti?cial intelligence, were not doing. After reading up on the literature, I have come to realize that it is not much different from conventional data analysis. The commonly used de?nition of knowledge discovery in databases: 'the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data' is actually in line with the core mission of conventional data analysis. The process employed by conventional data analysis is by no means trivial, and the patterns in data to be unraveled have, of course, to be valid, novel, useful and understandable. Therefore, what is the commotion all about? Careful scrutiny of the main lines of research in data mining and knowledge discovery again told me that they are not much different from that of conventional data analysis. Putting aside data warehousing and database m- agement aspects, again a main area of research in conventional database research, the rest of the tasks in data mining are largely the main concerns of conventional data analysis.

Inhalt

Acknowledgements 7
Preface 8
Contents 11
List of Figures 16
List of Tables 22
Introduction 25
1.1 On Spatial Data Mining and Knowledge Discovery 25
1.2 What Makes Spatial Data Mining Different 26
1.3 On Spatial Knowledge 27
1.4 On Spatial Data 28
1.5 Basic Tasks of Knowledge Discovery in Spatial Data 29
1.6 Issues of Knowledge Discovery in Spatial Data 34
1.7 Methodological Background for Knowledge Discovery in Spatial Data 35
1.8 Organization of the Book 36
Discovery of Intrinsic Clustering in Spatial Data 37
2.1 A Brief Background About Clustering 37
2.2 Discovery of Clustering in Space by Scale Space Filtering 41
2.2.1 On Scale Space Theory for Hierarchical Clustering 42
2.2.2 Hierarchical Clustering in Scale Space 44
2.2.3 Cluster Validity Check 49
2.2.4 Clustering Selection Rules 53
2.2.5 Some Numerical Examples 55
2.2.6 Discovering Land Covers in Remotely Sensed Images 56
2.2.7 Mining of Seismic Belts in Vector- Based Databases 60
2.2.8 Visualization of Temporal Seismic Activities via Scale Space Filtering 66
2.2.9 Summarizing Remarks on Clustering by Scale Space Filtering 70
2.3 Partitioning of Spatial Data by a Robust Fuzzy Relational Data Clustering Method 73
2.3.1 On Noise and Scale in Spatial Partitioning 74
2.3.2 Clustering Algorithm with Multiple Scale Parameters for Noisy Data 75
2.3.3 Robust Fuzzy Relational Data Clustering Algorithm 78
2.3.4 Numerical Experiments 81
2.4 Partitioning of Spatial Object Data by Unidimensional Scaling 2.4.1 A Note on the Use of Unidimensional Scaling 85
2.4.2 Basic Principle of Unidimensional Scaling in Data Clustering 86
2.4.3 Analysis of Simulated Data 88
2.4.4 UDS Clustering of Remotely Sensed Data 90
2.5 Unraveling Spatial Objects with Arbitrary Shapes Through Mixture Decomposition Clustering 2.5.1 On Noise and Mixture Distributions in Spatial Data 94
2.5.2 A Remark on the Mining of Spatial Features with Arbitrary Shapes 98
2.5.3 A Spatial-Feature Mining Model (RFMM) Based on Regression- Class Mixture Decomposition ( RCMD) 99
2.5.4 The RFMM with Genetic Algorithm (RFMM-GA) 102
2.5.5 Applications of RFMM-GA in the Mining of Features in Remotely Sensed Images 104
2.6 Cluster Characterization by the Concept of Convex Hull 2.6.1 A Note on Convex Hull and its Computation 108
2.6.2 Basics of the Convex Hull Computing Neural Network ( CHCNN) Model 110
2.6.3 The CHCNN Architecture 113
2.6.4 Applications in Cluster Characterization 118
Statistical Approach to the Identification of Separation Surface for Spatial Data 121
3.1 A Brief Background About Statistical Classification 121
3.2 The Bayesian Approach to Data Classification 124
3.2.1 A Brief Description of Bayesian Classification Theory 124
3.2.2 Naive Bayes Method and Feature Selection in Data Classification 125
3.2.3 The Application of Nai AE ve Bayes Discriminant Analysis in Client Segmentation for Product Marketing 126
3.2.4 Robust Bayesian Classification Model 136
3.3 Mixture Discriminant Analysis 3.3.1 A Brief Statement About Mixture Discriminant Analysis 137
3.3.2 Mixture Discriminant Analysis by Optimal Scoring 138
3.3.3 Analysis Results and Interpretations 139
3.4 The Logistic Model for Data Classification 3.4.1 A Brief Note About Using Logistic Regression as a Classifier 141
3.4.2 Data Manipulation for Client Segmentation 142
3.4.3 Logistic Regression Models and Strategies for Credit Card Promotion 143
3.4.4 Model Comparisons and Validations 149
3.5 Support Vector Machine for Spatial Classification 3.5.1 Support Vector Machine as a Classifier 154
3.5.2 Basics of Support Vector Machine 155
3.5.3 Experiments on Feature Extraction and Classification by SVM 160
Algorithmic Approach to the Identification of Classification Rules or Separation Surface for Spatial Data 167
4.1 A Brief Background About Algorithmic Classification 167
4.2 The Classification Tree Approach to the Discovery of Classification Rules in Data 4.2.1 A Brief Description of Classification and Regression tree ( CART) 169
4.2.2 Client Segmentation by CART 172
4.3 The Neural Network Approach to the Classification of Spatial Data 4.3.1 On the Use of Neural Networks in Spatial Classification 180
4.3.2 The Knowledge-Integrated Radial Basis Function (RBF) Model for Spatial Classification 183
4.3.3 An Elliptical Basis Function Network for Spatial Classification 196
4.4 Genetic Algorithms for Fuzzy Spatial Classification Systems 4.4.1 A Brief Note on Using GA to Discover Fuzzy Classification Rules 207
4.4.2 A General Framework of the Fuzzy Classification System 208
4.4.3 Fuzzy Rule Acquisition by GANGO 210
4.4.4 An Application in the Classification of Remote Sensing Data 218
4.5 The Rough Set Approach to the Discovery of Classification Rules in Spatial Data 4.5.1 Basic Ideas of the Rough Set Methodology for Knowledge Discovery 220
4.5.2 Basic Notions Related to Spatial Information Systems and Rough Sets 222
4.5.3 Interval-Valued Information Systems and Data Transformation 224
4.5.4 Knowledge Discovery in Interval-Valued Information Systems 226
4.5.5 Discovery of Classification Rules for Remotely Sensed Data 229
4.5.6 Classification of Tree Species with Hyperspectral Data 238
4.6 A Vision-Based Approach to Spatial Classification 4.6.1 On Scale and Noise in Spatial Data Classification 240
4.6.2 The Vision-Based Classification Method 242
4.6.3 Experimental Results 243
4.7 A Remark on the Choice of Classifiers 245
Discovery of Spatial Relationships in Spatial Data 246
5.1 On Mining Spatial Relationships in Spatial Data 246
5.2 Discovery of Local Patterns of Spatial Association 5.2.1 On the Measure of Local Variations of Spatial Associations 248
5.2.2 Local Statistics and their Expressions as a Ratio of Quadratic Forms 250
5.3 Dicovery of Spatial Non-Stationarity Based on the Geographically Weighted Regression Model 5.3.1 On Modeling Spatial Non- Stationarity within the Parameter- Varying Regression Framework 259
5.3.2 Geographically Weighted Regression and the Local–Global Issue About Spatial Non- Stationarity 261
5.3.3 Local Variations of Regional Industrialization in Jiangsu Province, P. R. China 267
5.3.4 Discovering Spatial Pattern of Influence of Extreme Temperatures on Mean Temperatures in China 273
5.4 Testing for Spatial Autocorrelation in Geographically Weighted Regression 277
5.5 A Note on the Extentions of the GWR Model 281
5.6 Discovery of Spatial Non-Stationarity Based on the Regression- Class Mixture Decomposition Method 5.6.1 On Mixture Modeling of Spatial Non- Stationarity in a Noisy Environment 283
5.6.2 The Notion of a Regression Class 285
5.6.3 The Discovery of Regression Classes under Noise Contamination 286
5.6.4 The Regression-Class Mixture Decomposition (RCMD) Method for knowledge Discovery in Mixed Distribution 290
5.6.5 Numerical Results and Observations 294
5.6.6 Comments About the RCMD Method 295
5.6.7 A Remote Sensing Application 298
5.6.8 An Overall View about the RCMD Method 299
Discovery of Structures and Processes in Temporal Data 300
6.1 A Note on the Discovery of Generating Structures or Processes of Time Series Data 300
6.2 The Wavelet Approach to the Mining of Scaling Phenomena in Time Series Data 6.2.1 A Brief Note on Wavelet Transform 302
6.2.2 Basic Notions of Wavelet Analysis 303
6.2.3 Wavelet Transforms in High Dimensions 308
6.2.4 Other Data Mining Tasks by Wavelet Transforms 309
6.2.5 Wavelet Analysis of Runoff Changes in the Middle and Upper Reaches of the Yellow River in China 309
6.2.6 Wavelet Analysis of Runoff Changes of the Yangtze River Basin 312
6.3 Discovery of Generating Structures of Temporal Data with Long- Range Dependence 6.3.1 A Brief Note on Multiple Scaling and Intermittency of Temporal Data 315
6.3.2 Multifractal Approach to the Identification of Intermittency in Time Series Data 316
6.3.3 Experimental Study on Intermittency of Air Quality Data Series 320
6.4 Finding the Measure Representation of Time Series with Intermittency 6.4.1 Multiplicative Cascade as a Characterization of the Time Series Data 324
6.4.2 Experimental Results 325
6.5 Discovery of Spatial Variability in Time Series Data 6.5.1 Multifractal Analysis of Spatial Variability Over Time 330
6.5.2 Detection of Spatial Variability of Rainfall Intensity 332
6.6 Identification of Multifractality and Spatio-Temperal Long Range Dependence in Multiscaling Remote Sensing 6.6.1 A Note on Multifractality and Long- Range Dependence in Remote Sensing Data 335
6.6.2 A Proposed Methodology for the Analysis of Multifractality and Long- Range Dependence in Remote Sensing Data 337
6.7 A Note on the Effect of Trends on the Scaling Behavior of Time Series with Long- Range Dependence 340
Summary and Outlooks 343
7.1 Summary 343
7.2 Directions for Further Research 7.2.1 Discovery of Hierarchical Knowledge Structure from Relational Spatial Data 344
7.2.2 Errors in Spatial Knowledge Discovery 346
7.2.3 Other Challenges 348
7.3 Concluding Remark 349
Bibliography 350
Author Index 372
Subject Index 378

Produkt-Details

• Erscheint lt. Verlag: 14.3.2010
• Reihe/Serie: Advances in Spatial Science
• Reihe/Serie: Advances in Spatial Science
• Zusatzinfo: XXIX, 360 p. 113 illus.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Geisteswissenschaften ► Geschichte ► Regional- / Ländergeschichte
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geografie / Kartografie
• Themenwelt: Sozialwissenschaften ► Soziologie
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Volkswirtschaftslehre
• Schlagworte: algorithm • classification • Clustering • Data Mining • geographical information system • geographic data • Knowledge Discovery • Remote Sensing • Spatial Data Mining
• ISBN-10: 3-642-02664-8 / 3642026648
• ISBN-13: 978-3-642-02664-5 / 9783642026645