Knowledge discovery in spatial data:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg
Springer
2010
|
| Series: | Advances in spatial science
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XXIX, 360 S. graph. Darst. |
| ISBN: | 9783642026638 |
Staff View
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| adam_text | 2.3.3 ROBUST FUZZY RELATIONAL DATA CLUSTERING ALGORITHM 54 CONTENTS 1
INTRODUCTION 1 1.1 ON SPATIAL DATA MINING AND KNOWLEDGE DISCOVERY 1 1.2
WHAT MAKES SPATIAL DATA MINING DIFFERENT 2 1.3 ON SPATIAL KNOWLEDGE 3
1.4 ON SPATIAL DATA 4 1.5 BASIC TASKS OF KNOWLEDGE DISCOVERY IN SPATIAL
DATA 5 1.6 ISSUES OF KNOWLEDGE DISCOVERY IN SPATIAL DATA 10 1.7
METHODOLOGICAL BACKGROUND FOR KNOWLEDGE DISCOVERY IN SPATIAL DATA 11 1.8
ORGANIZATION OF THE BOOK 12 2 DISCOVERY OF INTRINSIC CLUSTERING IN
SPATIAL DATA 13 2.1 A BRIEF BACKGROUND ABOUT CLUSTERING 13 2.2 DISCOVERY
OF CLUSTERING IN SPACE BY SCALE SPACE FILTERING 17 2.2.1 ON SCALE SPACE
THEORY FOR HIERARCHICAL CLUSTERING 18 2.2.2 HIERARCHICAL CLUSTERING IN
SCALE SPACE 20 2.2.3 CLUSTER VALIDITY CHECK 25 2.2.4 CLUSTERING
SELECTION RULES 29 2.2.5 SOME NUMERICAL EXAMPLES 31 2.2.6 DISCOVERING
LAND COVERS IN REMOTELY SENSED IMAGES 32 2.2.7 MINING OF SEISMIC BELTS
IN VECTOR-BASED DATABASES 36 2.2.8 VISUALIZATION OF TEMPORAL SEISMIC
ACTIVITIES VIA SCALE SPACE FILTERING 42 2.2.9 SUMMARIZING REMARKS ON
CLUSTERING BY SCALE SPACE FILTERING 46 2.3 PARTITIONING OF SPATIAL DATA
BY A ROBUST FUZZY RELATIONAL DATA CLUSTERING METHOD 49 2.3.1 ON NOISE
AND SCALE IN SPATIAL PARTITIONING 50 2.3.2 CLUSTERING ALGORITHM WITH
MULTIPLE SCALE PARAMETERS FOR NOISY DATA 51 BIBLIOGRAFISCHE
INFORMATIONEN HTTP://D-NB.INFO/994394594 DIGITALISIERT DURCH XVI
CONTENTS 2.3.4 NUMERICAL EXPERIMENTS 57 2.4 PARTITIONING OF SPATIAL
OBJECT DATA BY UNIDIMENSIONAL SCALING 61 2.4.1 A NOTE ON THE USE OF
UNIDIMENSIONAL SCALING 61 2.4.2 BASIC PRINCIPLE OF UNIDIMENSIONAL
SCALING IN DATA CLUSTERING 62 2.4.3 ANALYSIS OF SIMULATED DATA 64 2.4.4
UDS CLUSTERING OF REMOTELY SENSED DATA 66 2.5 UNRAVELING SPATIAL OBJECTS
WITH ARBITRARY SHAPES THROUGH MIXTURE DECOMPOSITION CLUSTERING 70 2.5.1
ON NOISE AND MIXTURE DISTRIBUTIONS IN SPATIAL DATA 70 2.5.2 A REMARK ON
THE MINING OF SPATIAL FEATURES WITH ARBITRARY SHAPES 74 2.5.3 A
SPATIAL-FEATURE MINING MODEL (RFMM) BASED ON REGRESSION-CLASS MIXTURE
DECOMPOSITION (RCMD) 75 2.5.4 THE RFMM WITH GENETIC ALGORITHM (RFMM-GA)
78 2.5.5 APPLICATIONS OF RFMM-GA IN THE MINING OF FEATURES IN REMOTELY
SENSED IMAGES 80 2.6 CLUSTER CHARACTERIZATION BY THE CONCEPT OF CONVEX
HULL 84 2.6.1 A NOTE ON CONVEX HULL AND ITS COMPUTATION 84 2.6.2 BASICS
OF THE CONVEX HULL COMPUTING NEURAL NETWORK (CHCNN) MODEL 86 2.6.3 THE
CHCNN ARCHITECTURE 89 2.6.4 APPLICATIONS IN CLUSTER CHARACTERIZATION 94
3 STATISTICAL APPROACH TO THE IDENTIFICATION OF SEPARATION SURFACE FOR
SPATIAL DATA 97 3.1 A BRIEF BACKGROUND ABOUT STATISTICAL CLASSIFICATION
97 3.2 THE BAYESIAN APPROACH TO DATA CLASSIFICATION 100 3.2.1 A BRIEF
DESCRIPTION OF BAYESIAN CLASSIFICATION THEORY 100 3.2.2 NAIVE BAYES
METHOD AND FEATURE SELECTION I CONTENTS 3.5 SUPPORT VECTOR MACHINE FOR
SPATIAL CLASSIFICATION 130 3.5.1 SUPPORT VECTOR MACHINE AS A CLASSIFIER
130 3.5.2 BASICS OF SUPPORT VECTOR MACHINE 131 3.5.3 EXPERIMENTS ON
FEATURE EXTRACTION AND CLASSIFICATION BY SVM 136 ALGORITHMIC APPROACH TO
THE IDENTIFICATION OF CLASSIFICATION RULES OR SEPARATION SURFACE FOR
SPATIAL DATA 143 4.1 A BRIEF BACKGROUND ABOUT ALGORITHMIC CLASSIFICATION
143 4.2 THE CLASSIFICATION TREE APPROACH TO THE DISCOVERY OF
CLASSIFICATION RULES IN DATA 145 4.2.1 A BRIEF DESCRIPTION OF
CLASSIFICATION AND REGRESSION TREE (CART) 145 4.2.2 CLIENT SEGMENTATION
BY CART 148 4.3 THE NEURAL NETWORK APPROACH TO THE CLASSIFICATION OF
SPATIAL DATA ... 156 4.3.1 ON THE USE OF NEURAL NETWORKS IN SPATIAL
CLASSIFICATION 156 4.3.2 THE KNOWLEDGE-INTEGRATED RADIAL BASIS FUNCTION
(RBF) MODEL FOR SPATIAL CLASSIFICATION 159 4.3.3 AN ELLIPTICAL BASIS
FUNCTION NETWORK FOR SPATIAL CLASSIFICATION 172 4.4 GENETIC ALGORITHMS
FOR FUZZY SPATIAL CLASSIFICATION SYSTEMS 183 4.4.1 A BRIEF NOTE ON USING
GA TO DISCOVER FUZZY CLASSIFICATION RULES 183 4.4.2 A GENERAL FRAMEWORK
OF THE FUZZY CLASSIFICATION SYSTEM 184 4.4.3 FUZZY RULE ACQUISITION BY
GANGO 186 4.4.4 AN APPLICATION IN THE CLASSIFICATION OF REMOTE SENSING
DATA 194 4.5 THE ROUGH SET APPROACH TO THE DISCOVERY OF CLASSIFICATION
RULES IN SPATIAL DATA 196 4.5.1 BASIC IDEAS OF THE ROUGH SET METHODOLOGY
FOR KNOWLEDGE DISCOVER XVIII CONTENTS 5 DISCOVERY OF SPATIAL
RELATIONSHIPS IN SPATIAL DATA 223 5.1 ON MINING SPATIAL RELATIONSHIPS IN
SPATIAL DATA 223 5.2 DISCOVERY OF LOCAL PATTERNS OF SPATIAL ASSOCIATION
225 5.2.1 ON THE MEASURE OF LOCAL VARIATIONS OF SPATIAL ASSOCIATIONS 225
5.2.2 LOCAL STATISTICS AND THEIR EXPRESSIONS AS A RATIO OF QUADRATIC
FORMS 227 5.3 DICOVERY OF SPATIAL NON-STATIONARITY BASED ON THE
GEOGRAPHICALLY WEIGHTED REGRESSION MODEL 236 5.3.1 ON MODELING SPATIAL
NON-STATIONARITY WITHIN THE PARAMETER-VARYING REGRESSION FRAMEWORK 236
5.3.2 GEOGRAPHICALLY WEIGHTED REGRESSION AND THE LOCAL-GLOBAL ISSUE
ABOUT SPATIAL NON-STATIONARITY 238 5.3.3 LOCAL VARIATIONS OF REGIONAL
INDUSTRIALIZATION IN JIANGSU PROVINCE, P.R. CHINA 244 5.3.4 DISCOVERING
SPATIAL PATTERN OF INFLUENCE OF EXTREME TEMPERATURES ON MEAN
TEMPERATURES IN CHINA 250 5.4 TESTING FOR SPATIAL AUTOCORRELATION IN
GEOGRAPHICALLY WEIGHTED REGRESSION 254 5.5 A NOTE ON THE EXTENTIONS OF
THE GWR MODEL 258 5.6 DISCOVERY OF SPATIAL NON-STATIONARITY BASED ON THE
REGRESSION-CLASS MIXTURE DECOMPOSITION METHOD 260 5.6.1 ON MIXTURE
MODELING OF SPATIAL NON-STATIONARITY IN A NOISY ENVIRONMENT 260 5.6.2
THE NOTION OF A REGRESSION CLASS 262 5.6.3 THE DISCOVERY OF REGRESSION
CLASSES UNDER NOISE CONTAMINATION 263 5.6.4 THE REGRESSION-CLASS MIXTURE
DECOMPOSITION (RCMD) METHOD FOR KNOWLEDGE DISCOVERY IN MIXED
DISTRIBUTION 267 5.6. CONTENTS XIX 6.2.6 WAVELET ANALYSIS OF RUNOFF
CHANGES OF THE YANGTZE RIVER BASIN 289 6.3 DISCOVERY OF GENERATING
STRUCTURES OF TEMPORAL DATA WITH LONG-RANGE DEPENDENCE 292 6.3.1 A BRIEF
NOTE ON MULTIPLE SCALING AND INTERMITTENCY OF TEMPORAL DATA 292 6.3.2
MULTIFRACTAL APPROACH TO THE IDENTIFICATION OF INTERMITTENCY IN TIME
SERIES DATA 293 6.3.3 EXPERIMENTAL STUDY ON INTERMITTENCY OF AIR QUALITY
DATA SERIES 297 6.4 FINDING THE MEASURE REPRESENTATION OF TIME SERIES
WITH INTERMITTENCY 301 6.4.1 MULTIPLICATIVE CASCADE AS A
CHARACTERIZATION OF THE TIME SERIES DATA 301 6.4.2 EXPERIMENTAL RESULTS
302 6.5 DISCOVERY OF SPATIAL VARIABILITY IN TIME SERIES DATA 307 6.5.1
MULTIFRACTAL ANALYSIS OF SPATIAL VARIABILITY OVER TIME 307 6.5.2
DETECTION OF SPATIAL VARIABILITY OF RAINFALL INTENSITY 309 6.6
IDENTIFICATION OF MULTIFRACTALITY AND SPATIO-TEMPERAL LONG RANGE
DEPENDENCE IN MULTISCALING REMOTE SENSING 312 6.6.1 A NOTE ON
MULTIFRACTALITY AND LONG-RANGE DEPENDENCE IN REMOTE SENSING DATA 312
6.6.2 A PROPOSED METHODOLOGY FOR THE ANALYSIS OF MULTIFRACTALITY AND
LONG-RANGE DEPENDENCE IN REMOTE SENSING DATA 314 6.7 A NOTE ON THE
EFFECT OF TRENDS ON THE SCALING BEHAVIOR OF TIME SERIES WITH LONG-RANGE
DEPENDENCE 317 7 SUMMARY AND OUTLOOKS 321 7.1 SUMMARY 32 ! 7.2
DIRECTIONS FOR FURTHER RESEARCH 322 7.2.1 DISCOVERY OF HIERARCHICAL
KNOWLEDGE STRUCTURE FROM RELATIONA
|
| any_adam_object | 1 |
| author | Leung, Yee |
| author_facet | Leung, Yee |
| author_role | aut |
| author_sort | Leung, Yee |
| author_variant | y l yl |
| building | Verbundindex |
| bvnumber | BV025599247 |
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| ctrlnum | (OCoLC)428028755 (DE-599)BVBBV025599247 |
| dewey-full | 910.285 |
| dewey-hundreds | 900 - History & geography |
| dewey-ones | 910 - Geography and travel |
| dewey-raw | 910.285 |
| dewey-search | 910.285 |
| dewey-sort | 3910.285 |
| dewey-tens | 910 - Geography and travel |
| discipline | Informatik Geographie |
| format | Book |
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| indexdate | 2024-07-09T22:37:14Z |
| institution | BVB |
| isbn | 9783642026638 |
| language | English |
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| physical | XXIX, 360 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
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| series2 | Advances in spatial science |
| spelling | Leung, Yee Verfasser aut Knowledge discovery in spatial data Yee Leung Berlin ; Heidelberg Springer 2010 XXIX, 360 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advances in spatial science Literaturangaben Raumdaten (DE-588)4206012-6 gnd rswk-swf Geoinformationssystem (DE-588)4261642-6 gnd rswk-swf Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Raumdaten (DE-588)4206012-6 s Wissensextraktion (DE-588)4546354-2 s Geoinformationssystem (DE-588)4261642-6 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020194862&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Leung, Yee Knowledge discovery in spatial data Raumdaten (DE-588)4206012-6 gnd Geoinformationssystem (DE-588)4261642-6 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| subject_GND | (DE-588)4206012-6 (DE-588)4261642-6 (DE-588)4546354-2 |
| title | Knowledge discovery in spatial data |
| title_auth | Knowledge discovery in spatial data |
| title_exact_search | Knowledge discovery in spatial data |
| title_full | Knowledge discovery in spatial data Yee Leung |
| title_fullStr | Knowledge discovery in spatial data Yee Leung |
| title_full_unstemmed | Knowledge discovery in spatial data Yee Leung |
| title_short | Knowledge discovery in spatial data |
| title_sort | knowledge discovery in spatial data |
| topic | Raumdaten (DE-588)4206012-6 gnd Geoinformationssystem (DE-588)4261642-6 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| topic_facet | Raumdaten Geoinformationssystem Wissensextraktion |
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