Leung Knowledge Discovery in Spatial Data

2009
ISBN: 978-3-642-02664-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 360 Seiten

Reihe: Advances in Spatial Science

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.

Autoren/Hrsg.

Leung, Yee

Inhaltsverzeichnis

1;Acknowledgements;7
2;Preface;8
3;Contents;11
4;List of Figures;16
5;List of Tables;22
6;Introduction;25
6.1;1.1 On Spatial Data Mining and Knowledge Discovery;25
6.2;1.2 What Makes Spatial Data Mining Different;26
6.3;1.3 On Spatial Knowledge;27
6.4;1.4 On Spatial Data;28
6.5;1.5 Basic Tasks of Knowledge Discovery in Spatial Data;29
6.6;1.6 Issues of Knowledge Discovery in Spatial Data;34
6.7;1.7 Methodological Background for Knowledge Discovery in Spatial Data;35
6.8;1.8 Organization of the Book;36
7;Discovery of Intrinsic Clustering in Spatial Data;37
7.1;2.1 A Brief Background About Clustering;37
7.2;2.2 Discovery of Clustering in Space by Scale Space Filtering;41
7.3;2.2.1 On Scale Space Theory for Hierarchical Clustering;42
7.4;2.2.2 Hierarchical Clustering in Scale Space;44
7.5;2.2.3 Cluster Validity Check;49
7.6;2.2.4 Clustering Selection Rules;53
7.7;2.2.5 Some Numerical Examples;55
7.8;2.2.6 Discovering Land Covers in Remotely Sensed Images;56
7.9;2.2.7 Mining of Seismic Belts in Vector- Based Databases;60
7.10;2.2.8 Visualization of Temporal Seismic Activities via Scale Space Filtering;66
7.11;2.2.9 Summarizing Remarks on Clustering by Scale Space Filtering;70
7.12;2.3 Partitioning of Spatial Data by a Robust Fuzzy Relational Data Clustering Method;73
7.13;2.3.1 On Noise and Scale in Spatial Partitioning;74
7.14;2.3.2 Clustering Algorithm with Multiple Scale Parameters for Noisy Data;75
7.15;2.3.3 Robust Fuzzy Relational Data Clustering Algorithm;78
7.16;2.3.4 Numerical Experiments;81
7.17;2.4 Partitioning of Spatial Object Data by Unidimensional Scaling 2.4.1 A Note on the Use of Unidimensional Scaling;85
7.18;2.4.2 Basic Principle of Unidimensional Scaling in Data Clustering;86
7.19;2.4.3 Analysis of Simulated Data;88
7.20;2.4.4 UDS Clustering of Remotely Sensed Data;90
7.21;2.5 Unraveling Spatial Objects with Arbitrary Shapes Through Mixture Decomposition Clustering 2.5.1 On Noise and Mixture Distributions in Spatial Data;94
7.22;2.5.2 A Remark on the Mining of Spatial Features with Arbitrary Shapes;98
7.23;2.5.3 A Spatial-Feature Mining Model (RFMM) Based on Regression- Class Mixture Decomposition ( RCMD);99
7.24;2.5.4 The RFMM with Genetic Algorithm (RFMM-GA);102
7.25;2.5.5 Applications of RFMM-GA in the Mining of Features in Remotely Sensed Images;104
7.26;2.6 Cluster Characterization by the Concept of Convex Hull 2.6.1 A Note on Convex Hull and its Computation;108
7.27;2.6.2 Basics of the Convex Hull Computing Neural Network ( CHCNN) Model;110
7.28;2.6.3 The CHCNN Architecture;113
7.29;2.6.4 Applications in Cluster Characterization;118
8;Statistical Approach to the Identification of Separation Surface for Spatial Data;121
8.1;3.1 A Brief Background About Statistical Classification;121
8.2;3.2 The Bayesian Approach to Data Classification;124
8.3;3.2.1 A Brief Description of Bayesian Classification Theory;124
8.4;3.2.2 Naive Bayes Method and Feature Selection in Data Classification;125
8.5;3.2.3 The Application of Nai AE ve Bayes Discriminant Analysis in Client Segmentation for Product Marketing;126
8.6;3.2.4 Robust Bayesian Classification Model;136
8.7;3.3 Mixture Discriminant Analysis 3.3.1 A Brief Statement About Mixture Discriminant Analysis;137
8.8;3.3.2 Mixture Discriminant Analysis by Optimal Scoring;138
8.9;3.3.3 Analysis Results and Interpretations;139
8.10;3.4 The Logistic Model for Data Classification 3.4.1 A Brief Note About Using Logistic Regression as a Classifier;141
8.11;3.4.2 Data Manipulation for Client Segmentation;142
8.12;3.4.3 Logistic Regression Models and Strategies for Credit Card Promotion;143
8.13;3.4.4 Model Comparisons and Validations;149
8.14;3.5 Support Vector Machine for Spatial Classification 3.5.1 Support Vector Machine as a Classifier;154
8.15;3.5.2 Basics of Support Vector Machine;155
8.16;3.5.3 Experiments on Feature Extraction and Classification by SVM;160
9;Algorithmic Approach to the Identification of Classification Rules or Separation Surface for Spatial Data;167
9.1;4.1 A Brief Background About Algorithmic Classification;167
9.2;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
9.3;4.2.2 Client Segmentation by CART;172
9.4;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
9.5;4.3.2 The Knowledge-Integrated Radial Basis Function (RBF) Model for Spatial Classification;183
9.6;4.3.3 An Elliptical Basis Function Network for Spatial Classification;196
9.7;4.4 Genetic Algorithms for Fuzzy Spatial Classification Systems 4.4.1 A Brief Note on Using GA to Discover Fuzzy Classification Rules;207
9.8;4.4.2 A General Framework of the Fuzzy Classification System;208
9.9;4.4.3 Fuzzy Rule Acquisition by GANGO;210
9.10;4.4.4 An Application in the Classification of Remote Sensing Data;218
9.11;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
9.12;4.5.2 Basic Notions Related to Spatial Information Systems and Rough Sets;222
9.13;4.5.3 Interval-Valued Information Systems and Data Transformation;224
9.14;4.5.4 Knowledge Discovery in Interval-Valued Information Systems;226
9.15;4.5.5 Discovery of Classification Rules for Remotely Sensed Data;229
9.16;4.5.6 Classification of Tree Species with Hyperspectral Data;238
9.17;4.6 A Vision-Based Approach to Spatial Classification 4.6.1 On Scale and Noise in Spatial Data Classification;240
9.18;4.6.2 The Vision-Based Classification Method;242
9.19;4.6.3 Experimental Results;243
9.20;4.7 A Remark on the Choice of Classifiers;245
10;Discovery of Spatial Relationships in Spatial Data;246
10.1;5.1 On Mining Spatial Relationships in Spatial Data;246
10.2;5.2 Discovery of Local Patterns of Spatial Association 5.2.1 On the Measure of Local Variations of Spatial Associations;248
10.3;5.2.2 Local Statistics and their Expressions as a Ratio of Quadratic Forms;250
10.4;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
10.5;5.3.2 Geographically Weighted Regression and the Local–Global Issue About Spatial Non- Stationarity;261
10.6;5.3.3 Local Variations of Regional Industrialization in Jiangsu Province, P. R. China;267
10.7;5.3.4 Discovering Spatial Pattern of Influence of Extreme Temperatures on Mean Temperatures in China;273
10.8;5.4 Testing for Spatial Autocorrelation in Geographically Weighted Regression;277
10.9;5.5 A Note on the Extentions of the GWR Model;281
10.10;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
10.11;5.6.2 The Notion of a Regression Class;285
10.12;5.6.3 The Discovery of Regression Classes under Noise Contamination;286
10.13;5.6.4 The Regression-Class Mixture Decomposition (RCMD) Method for knowledge Discovery in Mixed Distribution;290
10.14;5.6.5 Numerical Results and Observations;294
10.15;5.6.6 Comments About the RCMD Method;295
10.16;5.6.7 A Remote Sensing Application;298
10.17;5.6.8 An Overall View about the RCMD Method;299
11;Discovery of Structures and Processes in Temporal Data;300
11.1;6.1 A Note on the Discovery of Generating Structures or Processes of Time Series Data;300
11.2;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
11.3;6.2.2 Basic Notions of Wavelet Analysis;303
11.4;6.2.3 Wavelet Transforms in High Dimensions;308
11.5;6.2.4 Other Data Mining Tasks by Wavelet Transforms;309
11.6;6.2.5 Wavelet Analysis of Runoff Changes in the Middle and Upper Reaches of the Yellow River in China;309
11.7;6.2.6 Wavelet Analysis of Runoff Changes of the Yangtze River Basin;312
11.8;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
11.9;6.3.2 Multifractal Approach to the Identification of Intermittency in Time Series Data;316
11.10;6.3.3 Experimental Study on Intermittency of Air Quality Data Series;320
11.11;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
11.12;6.4.2 Experimental Results;325
11.13;6.5 Discovery of Spatial Variability in Time Series Data 6.5.1 Multifractal Analysis of Spatial Variability Over Time;330
11.14;6.5.2 Detection of Spatial Variability of Rainfall Intensity;332
11.15;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
11.16;6.6.2 A Proposed Methodology for the Analysis of Multifractality and Long- Range Dependence in Remote Sensing Data;337
11.17;6.7 A Note on the Effect of Trends on the Scaling Behavior of Time Series with Long- Range Dependence;340
12;Summary and Outlooks;343
12.1;7.1 Summary;343
12.2;7.2 Directions for Further Research 7.2.1 Discovery of Hierarchical Knowledge Structure from Relational Spatial Data;344
12.3;7.2.2 Errors in Spatial Knowledge Discovery;346
12.4;7.2.3 Other Challenges;348
12.5;7.3 Concluding Remark;349
13;Bibliography;350
14;Author Index;372
15;Subject Index;378