Spatial data mining: theory and application
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin ; Heidelberg
Springer
2015
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | xxviii, 308 Seiten Illustrationen 25 cm |
| ISBN: | 9783662485361 3662485362 9783662485385 |
Internformat
MARC
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|---|---|---|---|
| 001 | BV043890618 | ||
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| 020 | |a 3662485362 |9 3-662-48536-2 | ||
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| 100 | 1 | |a Li, Deren |d 1939- |e Verfasser |0 (DE-588)1030449201 |4 aut | |
| 245 | 1 | 0 | |a Spatial data mining |b theory and application |c Deren Li, Shuliang Wang, Deyi Li |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c 2015 | |
| 300 | |a xxviii, 308 Seiten |b Illustrationen |c 25 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Räumliches Datenbanksystem |0 (DE-588)4232580-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Geoinformation |0 (DE-588)4429674-5 |2 gnd |9 rswk-swf |
| 653 | |a Research | ||
| 653 | |a UNF | ||
| 653 | |a GIS data mining | ||
| 653 | |a Remote sensing image mininig | ||
| 653 | |a Big data clustering | ||
| 653 | |a Spatiotemporal video data mining | ||
| 653 | |a Cloud model | ||
| 653 | |a Data field | ||
| 653 | |a Spatial data mining | ||
| 653 | |a UYQ | ||
| 689 | 0 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | 1 | |a Geoinformation |0 (DE-588)4429674-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 1 | 1 | |a Räumliches Datenbanksystem |0 (DE-588)4232580-8 |D s |
| 689 | 1 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Wang, Shuliang |d 1974- |e Sonstige |0 (DE-588)1102188506 |4 oth | |
| 700 | 1 | |a Li, Deyi |d 1944- |e Sonstige |0 (DE-588)1102188735 |4 oth | |
| 710 | 2 | |a Springer-Verlag GmbH |0 (DE-588)1065168780 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |a Li |t Spatial Data Mining |
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Datensatz im Suchindex
| _version_ | 1809773078222209024 |
|---|---|
| adam_text |
CONTENTS
1
INTRODUCTION.
1
1.1 MOTIVATION FOR
SDM.
1
1.1.1 SUPERFLUOUS SPATIAL D A TA . 2
1.1.2 HAZARDS FROM SPATIAL D ATA. 4
1.1.3 ATTEMPTS TO UTILIZE DATA
.
6
1.1.4 PROPOSAL OF SD M .
8
1.2 THE STATE OF THE ART OF S D M
. 9
1.2.1 ACADEMIC ACTIVITIES
.
9
1.2.2 THEORETICAL TECHNIQUES . 10
1.2.3 APPLICABLE FIELDS
.
*
. 11
1.3 BOTTLENECK OF S D M
.
13
1.3.1 EXCESSIVE SPATIAL D ATA
.
13
1.3.2 HIGH-DIMENSIONAL SPATIAL DATA. 13
1.3.3 POLLUTED SPATIAL D A TA
.
14
1.3.4 UNCERTAIN SPATIAL D ATA
.
16
1.3.5 MINING DIFFERENCES. 17
1.3.6 PROBLEMS TO REPRESENT THE 17
1.3.7 MONOGRAPH CONTENTS AND STRUCTURES
. 18
1.4 BENEFITS TO A
READER. 20
REFERENCES.
20
2 SDM
PRINCIPLES.
23
2.1 SDM CONCEPTS
.
23
2.1.1 SDM CHARACTERISTICS. 23
2.1.2 UNDERSTANDING SDM FROM DIFFERER 25
2.1.3 DISTINGUISHING SDM FROM RELATED SUBJECTS . 26
2.1.4 SDM PYRAMID.
.
.
27
2.1.5 WEB SDM
.
. 29
2.2 FROM SPATIAL DATA TO SPATIAL KNOWLEDGE. 30
2.2.1 SPATIAL NUMERICAL.
30
2.2.2 SPATIAL D A TA
.
31
2.2.3 SPATIAL C ONCEPT
.
31
2.2.4 SPATIAL INFORMATION
.
2.2.5 SPATIAL KNOWLEDGE
.
33
2.2.6 UNIFIED A
CTION. 34
2.3 SDM SPACE
.
35
2.31 ATTRIBUTE
SPACE. 35
2.3.2 FEATURE
SPACE. 35
2.3.3 CONCEPTUAL SPACE
.
36
2.3.4 DISCOVERY STATE S PACE
.
36
2.4 SD M V IEW
.
38
2.4.1 SDM USER
.
38
2.4.2 SDM
METHOD.
39
2.4.3 SDM APPLICATION
.
39
2.4.4 SDM
HIERARCHY.
40
2.4.5 SDM
GRANULARITY.
42
2.4.6 SDM SCALE
.
43
2.4.7 DISCOVERY M ECHANISM
.
43
2.5 SPATIAL KNOWLEDGE TO D
ISCOVER. 45
2.5.1 GENERAL GEOMETRIC RULE AND SPATIAL ASSOCIATION R ULE. . . 45
2.5.2 SPATIAL CHARACTERISTICS RULE AND !^SCRIMINATE RULE
. 48
2.5.3 SPATIAL CLUSTERING RULE AND CLARIFICATION RULE
. 48
2.5.4 SPATIAL PREDICTABLE RULE AND SERIAL RULE. 49
2.5.5 SPATIAL EXCEPTION OR OUTLIER
.
50
2.6 SPATIAL KNOWLEDGE REPRESENTATIE
.
51
2.6.1 NATURAL
LANGUAGE. 51
2.6.2 CONVERSION BETWEEN QUANTITATIVE DATA
AND QUALITATIVE CONCEPI. 52
2.6.3 SPATIAL KNOWLEDGE MEASUREMENT
.
53
2.6.4 SPATIAL RULES PLUS EXCEPTIONS
.
54
REFERENCES.
55
3 SDM DATA S O U RC E
.
57
3.1 CONTENTS AND C H ICTERISTICS OF SPATIAL DATA. 57
3.1.1
SPATIALOBJECTS.
57
3.1.2 CONTENTS OF SPATIAL DATA
.
58
3.1.3 CHARACTERISTICS OF SPATIAL DATA
.
60
3.1.4 DIVERSITY OF SPATIAL D A TA
.
61
3.1.5 SPATIAL DATA F U SIO N
.
62
3.1.6 SEAMLESS ORGANIZATION OF SPATIAL DATA
. 64
3.2 SPATIAL DATA
ACQUISITION.
65
3.2.1 POINT
ACQUISITION. 66
3.2.2 AREA
ACQUISITION. 67
3.2.3 MOBILITY ACQUISITION
.
69
3.3 SPATIAL DATA FORM
ATS.
71
3.3.1 VECTOR
DATA.
72
3.3.2 RASTER
DATA.
72
3.3.3 VECTOR-RASTER D ATA
.
72
3.4 SPATIAL DATA MODEL
.
75
3.4.1 H IE RIH IC A L MODEL AND NETWORK
MODEL. 76
3.4.2 RELATIONAL M ODEL
.
.
76
3.4.3 OBJECT-ORIENTED M O D E L
.
78
3.5 SPATIAL
DATABASES.
81
3.5.1 SURVEYING AND MAPPING DATABASE.
81
3.5.2 DEM DATABASE WITH HIERARCHY
. 83
3.5.3 IMAGE
PYRAMID.
84
3.6 SPATIAL DATA
WAREHOUSE.
86
3.6.1 DATA
WAREHOUSE.
87
3.6.2 SPATIAL DATA
CUBES. 87
3.6.3 SPATIAL DATA WAREHOUSE FOR DATA M INING. . 89
3.7 NATIONAL SPATIAL DATA I N F R A ^
.
90
3.7.1 AMERICAN NATIONAL SPATIAL DATA 1^^^ 90
3.7.2 GEOSPATIAL DATA SYSTEM OF GREAT BRITAIN
ORDNANCE S
URVEY. 96
3.7.3 GERMAN AUTHORITATIVE TOPOGRAPHIC-CARTOGRAPHIC
INFORMATION SYSTEM
. 96
3.7.4 CANADIAN NATIONAL TOPOGRAPHIC DATA BASE (N T* B ). 97
3.7.5 AUSTRALIAN LAND AND GEOGRAPHIC
INFORMATION SYSTEM
. 98
3.7.6 JAPANESE GEOGRAPHIC INFORMATION SYSTEM. 98
3.7.7 ASIA-PACIFIC SPATIAL DATA INFRASTRUCTURE. 99
3.7.8 EUROPEAN SPATIAL DATA INFRAST^^^ 100
3.8 CHINA*S NATIONAL SPATIAL DATA INFRASTRUCTURE. 102
3.8.1 CNSDINECESSITYANDPOSSIBILITY. 102
3.8.2 CNSDI CONTENTS. 102
3 8.3 CNGDF OFC N S D I.
.
.
104
3.8.4 CSDTS O FC N S D
I. 105
3.9 FROM GGDI TO BIG
DATA.
107
3.9.1 G G D I
.
*
.
107
3.9.2 DIGITALEARTH. 109
3.9.3 SMART PLANET
.
110
3.9.4 BIG D
ATA.
I L L
3.10 SPATIAL DATA AS A
SERVICE.
113
REFERENCES.
117
4 SPATIAL D ATA
CLEANING.
119
4.1 PROBLEMS IN SPATIAL D A TA
. 119
4.1.1 POLLUTED SPATIAL D A TA
. 120
4.1.2 OBSERVATION ERRORS IN SPATIAL DATA. 123
4.1.3 MODEL ERRORS ON SPATIAL DATA
.
126
4.2 THE STATE OF THE A R T
.
129
4.21 STAGES OF SPATIAL DATA ERROR PROCESSING. 129
4.2.2 THE UNDERDEVELOPMENT OF SPATIAL DATA CLEANING . 131
4.3 CHARACTERISTICS AND CONTENTS OF SPATIAL DATA CLEANING .
4.3.1 FUNDAMENTAL CHARACTERISTICS
.
.
4.3.2 ESSENTIAL C
ONTENTS.
4.4 SYSTEMATIC ERROR
CLEANING.
.
4.4.1 DIRECT COMPENSATION M ETH O D
.
4.4.2 INDIRECT COMPENSATION M
ETHOD.
4.5 STOCHASTIC ERROR C
LEANING.
4.5.1 FUNCTION M
ODEL.
4.5.2 RANDOM MODEL
.
4.5.3 ESTIMATION
EQUATION.
4.5.4 VARIOUS SPECIAL CIRCUM
STANCES.
4.6 GROSS ERROR CLEANING
.
4.6.1 THE RELIABILITY OF THE ADJUSTMENT SYSTEM.
4.6.2 DATA SNOOPING
.
4.6.3 THE ITERATION METHOD WITH SELECTED
WEIGHTS.
4.6.4 ITERATION WITH THE SELECTED WEIGHTS
FROM ROBUST ESTIMATION
.
4.6.5 ITERATION SUPERVISED BY POSTERIORI VARIANCE ESTIMATION.
4.7 GRAPHIC AND IMAGE
CLEANING.
4.7.1 THE CORRECTION OF RADIATION DEFORMATION.
4.7.2 THE CORRECTION OF GEOMETRIC DEFORMATION .
4.7.3 A CASE OF IMAGE
CLEANING.
REFERENCES.
5 METHODS AND TECHNIQUES IN 8
*
.
5.1 CRISP SET T H EO RY
.
5.1.1 PROBABILITY
THEORY.
5.1.2 EVIDENCE T H EO RY
.
5.1.3 SPATIAL STATISTICS
.
5.1.4 SPATIAL
CLUSTERING.
5.1.5
SPATIALANALYSIS.
5.2 EXTENDED SET T HEORY
.
5.2.1 FUZZY S E TS
.
5.2.2 ROUGH S ETS
.
.
5.3 BIONIC M
ETHOD.
5.3.1 ARTIFICIAL NEURAL N ETW ORK
.
5.3.2 GENETIC ALGORITHM
S.
5.4
OTHERS.
5.4.1 RULE
INDUCTION.
5.4.2 DECISION
TREES.
5.4.3 VISUALIZATION TECHNIQUES
.
5.5 DISCUSSION .
5.5.1
COMPARISONS.
5.5.2 USABILITY
.
.
REFERENCES.
33
133
133
134
35
*
36
*
7
*
37
*
37
*
38
*
39
*
42
*
4
**
45
*
45
*
46
*
49
*
51
*
51,
*
54
*
55
55
*
57
*
57
57
59
60
61
61
62
63
64
6
'
5
65
66
67
67
69
69
69
70
70
71
H
L
L
F
J
H
H
H
L
L
H
H
H
H
H
H
1
1
1
1
1
1
1
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
R
L
6 DATA
FIELD.
175
6.1 FROM A PHYSICAL FIELD TO A DATA F IE LD
. 175
6.1.1 FIELD IN PHYSICAL S P A C E
.
176
6.1.2 FIELD IN DATA S P A C E
. 177
6.2 FUNDAMENTAL DEFOITIONS OF . 178
6.2.1 NECESSARY CONDITIONS
.
178
6.2.2 MATHEMATICAL M O D E L
.
179
6.2.3 M
ASS.
179
6.2.4 UNIT POTENTIAL F UNCTION
.
180
6.2.5 IMPACT
FACTOR.
181
6.3 DEPICTION OF DATA F IE LD
. 182
6.3.1 FIELD L
INES.
182
6.3.2 EQUIPOTENTIALLINE(SURFA^^
. 182
6.3.3 TOPOLOGICAL C
LUSTER. 184
REFERENCES.
185
7 CLOUD M O D E
L.
187
7.1 DEFINITION AND
PROPERTY.
187
7.1.1 CLOUD AND CLOUD D ROPS
.
187
7.1.2 PROPERTIES
.
188
7.1.3 INTEGRATING RANDOMNESS AND F U Z Z N 188
7.2 THE NUMERICAL C H ICTERISTICS OF A CLOUD. 189
7.3 THE TYPES OF CLOUD
MODELS. 190
7.4 CLOUD
GENERATOR.
192
7.4.1 FORWARD CLOUD GENERATOR
.
192
7.4.2 BACKWARD CLOUD
GENERATOR. 194
7.4.3 PRECONDITION CLOUD
GENERATOR. 196
7.5 UNCERTAINTY
REASONING.
196
7.5.1 ONE-RULE R EASONING
.
197
7.5.2 MULTI-RULE REASONING
.
198
REFERENCES.
201
8 GIS DATA M
INING.
203
8.1 SPATIAL ASSOCIATION RULE M INING
.
203
8.1.1 THE MINING PROCESS OF ASSOCIATION RULE. 204
8.1.2 ASSOCIATION RULE MINING WITH APRIORI
AGORITHM. 205
8.1.3 ASSOCIATION RULE MINING WITH CONCEPT LATTICE. 207
8.1.4 ASSOCIATION RULE MINING WITH A CLOUD MODEL . 211
8.2 SPATIAL DISTRIBUTION RULE MINING WITH INDUCTIVE LEARNING . 215
8.3 ROUGH SET-BASED DECISION AND KNOWLEDGE DISCOVERY. 222
8.3.1 ATTRIBUTE IM PORTANCE
.
223
8.3.2 URBAN TEMPERATURE DATA MINING. 224
8.4 SPATIAL CLUSTERING
.
231
8.4.1 HIERARCHICAL CLUSTERING WITH DATA FIELDS. 233
8.4.2 FUZZY COMPREHENSIVE CLUSTERING.
235
8.4.3 MATHEMATICAL MORPHOLOGY CLUSTERING. 243
8.5 LANDSLIDE
MONITORING.
245
8.5.1 SDM VIEWS OF LANDSLIDE MONITORING DATA M INING . 245
8.5.2 PAN-CONCEPT HIERARCHY T RE E
.
248
8.5.3 NUMERICAL CHARACTERS AND RULES . 248
8.5.4 RULES PLUS
EXCEPTIONS. 253
REFERENCES.
255
9 SENSING IMAGE M IN IN G
. 257
9.1 RS IMAGE
PREPROCESSING.
257
9.1.1 ROUGH SET-BASED IMAGE FILTER
.
258
9.1.2 ROUGH SETSASED IMAGE ENHANCEMENT . 258
9.2 RS IMAGE
CLASSIFICATION.
260
9.2.1 INDUCTIVE LEARNING-BASED IMAGE CLASSIFICATION . 260
9.2.2 ROUGH SET-BASED IMAGE CLASSIFICATION . 264
9.2.3 ROUGH SET-BASED THEMATIC EXTO^ . 267
9.3 RS IMAGE R
ETRIEVAL.
268
9.3.1 FEATURES FOR IMAGE R ETRIEVAL
.
268
9.3.2 SEMIVARIOGRAM-BASED PARAMETER TO DESCRIBE
IMAGE SIMILARITY
.
269
9.3.3 IMAGE RETRIEVAL FOR DETECTING TRAIN DEFORMATION. 271
9.4 FACIAL EXPRESSION IMAGE M
INING. 274
9.4.1 CLOUD MODEL-BASED FACIAL EXPRESSION IDENTIFICATION. . . 275
9.4.2 DATA FIELD-BASED HUMAN FACIAL
EXPRESSION
RECOGNITION. 278
9.5 BRIGHTNESS OF NIGHTTIME LIGHT IMAGES AS A PROXY. 281
9.5.1 BRIGHTNESS OF NIGHTTIME LIGHTS AS A PROXY
FOR FREIGHT TRAFFIC
.
282
9.5.2 EVALUATING THE SYRIAN CRISIS WITH NIGHTTIME
LIGHT
IMAGES.
284
9.5.3 NIGHTTIME LIGHT DYNAMICS IN THE BELT AND ROAD. 288
9.6 SPATIOTEMPORAL VIDEO DATA M INING
.
290
9.6.1 TECHNICAL DIFFICULTIES IN SPATIOTEMPORAL
VIDEO DATA
MINING. 291
9.6.2 INTELLIGENT VIDEO DATA COMPRESSION
AND CLOUD
STORAGE. 292
9.6.3 CONTENT-BASED VIDEO R
ETRIEVAL. 292
9.6.4 VIDEO DATA MINING UNDER SPATIOTEMPORAL DISTOBUTION. . . 293
REFERENCES.
296
10 SDM
SYSTEMS.
299
10.1 OLSDBMINERFORGISDATA. . . 299
10.2 RSIMAGEMINER FOR IMAGE DATA
.
.
300
10.3 SPATIOTEMPORAL VIDEO D A 304
10.4 EVERY
DATA.
305
REFERENCES.
308 |
| any_adam_object | 1 |
| author | Li, Deren 1939- |
| author_GND | (DE-588)1030449201 (DE-588)1102188506 (DE-588)1102188735 |
| author_facet | Li, Deren 1939- |
| author_role | aut |
| author_sort | Li, Deren 1939- |
| author_variant | d l dl |
| building | Verbundindex |
| bvnumber | BV043890618 |
| classification_tum | RPL 017f DAT 620f |
| ctrlnum | (OCoLC)920691020 (DE-599)DNB1075880203 |
| dewey-full | 006.312 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.312 |
| dewey-search | 006.312 |
| dewey-sort | 16.312 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Raumplanung Geographie |
| format | Book |
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| id | DE-604.BV043890618 |
| illustrated | Illustrated |
| indexdate | 2024-09-10T02:08:22Z |
| institution | BVB |
| institution_GND | (DE-588)1065168780 |
| isbn | 9783662485361 3662485362 9783662485385 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029300021 |
| oclc_num | 920691020 |
| open_access_boolean | |
| owner | DE-12 DE-91G DE-BY-TUM |
| owner_facet | DE-12 DE-91G DE-BY-TUM |
| physical | xxviii, 308 Seiten Illustrationen 25 cm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| spelling | Li, Deren 1939- Verfasser (DE-588)1030449201 aut Spatial data mining theory and application Deren Li, Shuliang Wang, Deyi Li Berlin ; Heidelberg Springer 2015 xxviii, 308 Seiten Illustrationen 25 cm txt rdacontent n rdamedia nc rdacarrier Räumliches Datenbanksystem (DE-588)4232580-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Geoinformation (DE-588)4429674-5 gnd rswk-swf Research UNF GIS data mining Remote sensing image mininig Big data clustering Spatiotemporal video data mining Cloud model Data field Spatial data mining UYQ Data Mining (DE-588)4428654-5 s Geoinformation (DE-588)4429674-5 s DE-604 Räumliches Datenbanksystem (DE-588)4232580-8 s 1\p DE-604 Wang, Shuliang 1974- Sonstige (DE-588)1102188506 oth Li, Deyi 1944- Sonstige (DE-588)1102188735 oth Springer-Verlag GmbH (DE-588)1065168780 pbl Erscheint auch als Online-Ausgabe Li Spatial Data Mining B:DE-101 application/pdf http://d-nb.info/1075880203/04 Inhaltsverzeichnis X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=544741536eeb4d0e857245d0c7f9ac8b&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029300021&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Li, Deren 1939- Spatial data mining theory and application Räumliches Datenbanksystem (DE-588)4232580-8 gnd Data Mining (DE-588)4428654-5 gnd Geoinformation (DE-588)4429674-5 gnd |
| subject_GND | (DE-588)4232580-8 (DE-588)4428654-5 (DE-588)4429674-5 |
| title | Spatial data mining theory and application |
| title_auth | Spatial data mining theory and application |
| title_exact_search | Spatial data mining theory and application |
| title_full | Spatial data mining theory and application Deren Li, Shuliang Wang, Deyi Li |
| title_fullStr | Spatial data mining theory and application Deren Li, Shuliang Wang, Deyi Li |
| title_full_unstemmed | Spatial data mining theory and application Deren Li, Shuliang Wang, Deyi Li |
| title_short | Spatial data mining |
| title_sort | spatial data mining theory and application |
| title_sub | theory and application |
| topic | Räumliches Datenbanksystem (DE-588)4232580-8 gnd Data Mining (DE-588)4428654-5 gnd Geoinformation (DE-588)4429674-5 gnd |
| topic_facet | Räumliches Datenbanksystem Data Mining Geoinformation |
| url | http://d-nb.info/1075880203/04 http://deposit.dnb.de/cgi-bin/dokserv?id=544741536eeb4d0e857245d0c7f9ac8b&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029300021&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT lideren spatialdataminingtheoryandapplication AT wangshuliang spatialdataminingtheoryandapplication AT lideyi spatialdataminingtheoryandapplication AT springerverlaggmbh spatialdataminingtheoryandapplication |
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