Quality driven database mining:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Aachen
Shaker
2001
|
| Schriftenreihe: | Berichte aus der Informatik
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zugl.: München, Univ., Fak. für Mathematik und Informatik, Diss., 2001 |
| Beschreibung: | X, 253 S. Ill., graph. Darst. |
| ISBN: | 3826585593 |
Internformat
MARC
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| 100 | 1 | |a Breunig, Markus M. |d 1971- |e Verfasser |0 (DE-588)122803299 |4 aut | |
| 245 | 1 | 0 | |a Quality driven database mining |c Markus M. Breunig |
| 264 | 1 | |a Aachen |b Shaker |c 2001 | |
| 300 | |a X, 253 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Berichte aus der Informatik | |
| 500 | |a Zugl.: München, Univ., Fak. für Mathematik und Informatik, Diss., 2001 | ||
| 650 | 7 | |a Hochschulschrift |2 gtt | |
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Datensatz im Suchindex
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| adam_text |
VII
TABLE
OF
CONTENTS
ACKNOWLEDGMENTS
.
I
ABSTRACT
.
III
ABSTRACT
(IN
DEUTSCH)
.
V
TABLE
OF
CONTENTS
.VII
1
INTRODUCTION
1
1.1
KNOWLEDGE
DISCOVERY
IN
DATABASES
.
2
1.2
DATA
MINING
.
5
1.3
CLUSTERING
.
8
1.4
OUTLIER
DETECTION
.
9
1.5
OUTLINE
OF
THE
THESIS
.
11
2
EXPLORING
THE
STRUCTURE
OF
FEATURE
SPACES
15
2.1
FEATURE
SPACES
AND
SIMILARITY
MEASURES
.
16
2.1.1
EXAMPLES
OF
APPLICATIONS
AND
FEATURE
TRANSFORMATIONS
.
.
16
2.1.1.1
GEOMETRICAL
SHAPES
IN
CAD
DATABASES
.
17
2.1.1.2
COLOR
HISTOGRAMS
IN
IMAGE
DATABASES
.
20
2.1.1.3
TIME
SEQUENCES
.
21
2.1.1.4
SPECTRAL
CHANNELS
IN
SATELLITE
IMAGE
DATABASES
.
22
2.1.1.5
SHAPES
IN
GRAY-SCALE
IMAGE
DATABASES
.
24
2.1.2
SPACES
AND
DISTANCES
.
26
2.1.2.1
FEATURE
SPACE
.
26
2.1.2.2
METRICSPACE
.
27
2.1.2.3
VECTORSPACE
.
28
2.1.2.4
COMMON
DISTANCES
ON
VECTOR
SPACES
.
28
2.1.2.5
DATABASES
.
29
2.2
EFFICIENT
QUERY
PROCESSING
IN
FEATURE
SPACES
.
31
2.2.1
SIMILARITY
QUERY
TYPES
.
31
2.2.1.1
RANGE
QUERIES
.
31
2.2.1.2
^-NEAREST
NEIGHBOR
QUERIES
.
32
2.2.1.3
NON-DETERMINISTIC
FC-NEAREST
NEIGHBOR
QUERIES
.
33
2.2.1.4
SIMILARITY
QUERIES
IN
SUMMARY
.
35
2.2.2
ANSWERING
SIMILARITY
QUERIES
.
36
2.2.2.1
WITHOUT
INDEX
SUPPORT
.
36
2.2.2.2
WITH
INDEX
SUPPORT
.
37
VIII
2.2.2.3
REVIEW
OF
IMPORTANT
ACCESS
METHODS
.
41
2.3
CLUSTERING
.
49
2.3.1
PARTITIONING
CLUSTERING
ALGORITHMS
.
51
2.3.1.1
OPTIMIZATION
BASED
ALGORITHMS
.
51
2.3.1.2
DENSITY-BASED
TECHNIQUES
.
54
2.3.2
HIERARCHICAL
CLUSTERING
ALGORITHMS
.
58
2.3.2.1
SINGLE-LINK
AND
ITS
VARIANTS
.
58
2.3.2.2
CURE
.
60
2.3.2.3
GRID
AND
BANG
CLUSTERING
.
61
2.3.2.4
CHAMELEON
.
61
2.3.2.5
BIRCH
AND
BUBBLE
.
61
2.4
OUTLIER
DETECTION
.
63
2.4.1
DISTRIBUTION-BASED
TECHNIQUES
.
64
2.4.2
DEPTH-BASED
TECHNIQUES
.
65
2.4.3
DISTANCE-BASED
OUTLIERS
.
67
2.4.4
OTHER
APPROACHES
.
68
2.4.5
FRAUD
DETECTION
.
69
2.5
SUMMARY
.
70
3
THE
THEORY
OF
DENSITY-BASED
HIERARCHICAL
CLUSTER
ORDERING
71
3.1
FLAT
DENSITY-BASED
DECOMPOSITION
OF
A
DATABASE
.
72
3.1.1
(DIRECT)
DENSITY-REACHABILITY,
CLUSTERS
AND
NOISE
.
72
3.1.2
DENSITY-BASED
PARTITIONING
CLUSTERING
ALGORITHM
.
76
3.2
FROM
FLAT
CLUSTERING
TO
HIERARCHICAL
CLUSTER
ORDERING
.
77
3.2.1
CENTRAL
IDEA
OF
HIERARCHICAL
CLUSTER
ORDERING
.
79
3.2.2
CORE-DISTANCE
.
80
3.2.3
REACHABILITY-DISTANCE
.
81
3.2.4
CLUSTER
ORDERING
.
83
3.3
EQUIVALENCE
OF
CLUSTERS
AND
CLUSTER
INTERVALS
.
85
3.3.1
CLUSTER
INTERVAL
.
85
3.3.2
EQUIVALENCE
OF
CLUSTER
INTERVALS
AND
CLUSTERS
.
86
3.4
CONSTRUCTING
A
CLUSTER
ORDERING
.
94
3.5
SUMMARY
.
99
4
COMPUTING
AND
ANALYZING
THE
HIERARCHICAL
CLUSTER
ORDERING
101
4.1
COMPUTING
A
DENSITY-BASED
CLUSTER
ORDERING
.
102
4.2
VISUALLY
ANALYZING
THE
CLUSTERING
ORDERING
.
105
4.2.1
REACHABILITY-PLOTS
AND
PARAMETERS
.
105
4.2.2
EXPERIMENTAL
EVALUATION
.
108
4.3
AUTOMATIC
EXTRACTING
OF
CLUSTERING
INFORMATION
.
ILL
IX
4.3.1
EXTRACTING
FLAT
PARTITIONINGS
.
ILL
4.3.2
EXTRACTING
THE
INTRINSIC,
HIERARCHICAL
CLUSTERING
STRUCTURE
.
113
4.3.2.1
CONCEPTS
AND
FORMAL
DEFINITION
OF
A
CLUSTER
.
.113
4.3.2.2
AN
EFFICIENT
ALGORITHM
TO
COMPUTE
ALL
^-CLUSTERS
118
4.3.2.3
EXPERIMENTAL
EVALUATION
.
120
4.4
SUMMARY
.
123
5
PROCESSING
TIME
OPTIMIZATION
USING
SIMILARITY
JOINS
125
5.1
MOTIVATION
.
126
5.2
REPEATED
SIMILARITY
QUERY
BASED
METHODS
.
128
5.3
SIMILARITY
JOIN
ALGORITHMS
.
129
5.4
SIMILARITY-JOIN
BASED
CLUSTERING
.
131
5.4.1
GENERAL
IDEA
.
131
5.4.2
APPLICATION
TO
OPTICS
.
136
5.5
EXPERIMENTAL
EVALUATION
.
139
5.5.1
PAGE
SIZE
.
139
5.5.2
DATABASE
SIZE
.
141
5.5.3
QUERY
RANGE
.
142
5.6
SUMMARY
.
144
6
SCALING
UP
DENSITY-BASED
HIERARCHICAL
CLUSTERING
145
6.1
MOTIVATION
.
146
6.2
RANDOM
SAMPLING
AND
CF
CENTERS
.
149
6.2.1
RANDOM
SAMPLING
.
149
6.2.2
CLUSTERING
FEATURE
CENTERS
.
150
6.2.3
DISCUSSION
.
151
6.3
POST-PROCESSING
.
153
6.3.1
RANDOM
SAMPLING
PLUS
POST-PROCESSING
.
155
6.3.2
CLUSTERING
FEATURE
CENTERS
PLUS
POST-PROCESSING
.
156
6.3.3
DISCUSSION
.
157
6.4
DATA
BUBBLES
.
159
6.4.1
DEFINITION
OF
DATA
BUBBLES
.
160
6.4.2
GENERATING
DATA
BUBBLES
.
160
6.4.3
ESTIMATING
REACHABILITY
VALUES
.
162
6.4.4
MODIFYING
THE
POST-PROCESSING
FOR
DATA
BUBBLES
.
.
.
.165
6.4.5
SEEDED
DATA
BUBBLES
-
THE
BS-ALGORITHM
.
166
6.4.6
DATA
BUBBLES
FROM
BIRCH
-
THE
BB-ALGORITHM
.
168
6.4.7
DISCUSSION
.
170
6.5
EXPERIMENTAL
COMPARISON
OF
THE
ALGORITHMS
.
172
6.5.1
PERFORMANCE
EVALUATION
.
172
X
6.5.2
QUALITY
EVALUATION
.
176
6.5.3
DISCUSSION
.
180
6.5.4
REAL-WORLD
DATABASE
.
182
6.6
SUMMARY
.
185
7
THE
THEORY
OF
DENSITY-BASED
LOCAL
OUTLIER
FACTORS
187
7.1
MOTIVATION
.
188
7.2
PROBLEMS
OF
EXISTING
(NON-LOCAL)
APPROACHES
.
189
7.3
FORMAL
DEFINITION
OF
LOCAL
OUTLIERS
.
191
7.4
PROPERTIES
OF
LOCAL
OUTLIERS
.
194
7.4.1
LOF
FOR
OBJECTS
DEEP
IN
A
CLUSTER
.
194
7.4.2
A
GENERAL
UPPER
AND
LOWER
BOUND
ON
LOF
.
196
7.4.3
THE
TIGHTNESS
OF
THE
BOUNDS
.
199
7.4.4
GENERALIZATION
OF
THE
BOUNDS
TO
MULTIPLE-CLUSTER
OUTLIERS
.
203
7.5
THE
IMPACT
OF
THE
PARAMETER
MINPTS
.
206
7.5.1
HOW
LOF
VARIES
ACCORDING
TO
CHANGING
MINPTS
VALUES
.
207
7.5.2
INTERPRETATIONS
OF
THE
PARAMETER
MINPTS
.
209
7.5.3
DETERMINING
A
RANGE
OF
MINPTS
VALUES
.
210
7.6
SUMMARY
.
212
8
COMPUTING
AND
ANALYZING
DENSITY-BASED
LOCAL
OUTLIERS
213
8.1
VISUALIZATION
OF
THE
OUTLIER
FACTORS
.
214
8.2
COMBINING
OUTLIER
FACTORS
AND
REACHABILITY-PLOTS
.
215
8.3
HOCKEY
DATA
.
217
8.4
SOCCER
DATA
.
218
8.5
64-DIM
COLOR
HISTOGRAMS
.
219
8.6
PERFORMANCE
.
219
8.7
SUMMARY
.
222
9
CONCLUSIONS
223
9.1
PROBLEM
STATEMENT
.
224
9.2
CONTRIBUTIONS
.
226
9.3
FUTURE
DIRECTIONS
.
229
REFERENCES
.
231
INDEX
.
243
LIST
OF
FIGURES
.
247
LIST
OF
DEFINITIONS
.
251
CURRICULUM
VITAE
.
253 |
| any_adam_object | 1 |
| author | Breunig, Markus M. 1971- |
| author_GND | (DE-588)122803299 |
| author_facet | Breunig, Markus M. 1971- |
| author_role | aut |
| author_sort | Breunig, Markus M. 1971- |
| author_variant | m m b mm mmb |
| building | Verbundindex |
| bvnumber | BV013643886 |
| classification_rvk | ST 270 ST 530 |
| classification_tum | MAT 627d DAT 450d |
| ctrlnum | (OCoLC)47307105 (DE-599)BVBBV013643886 |
| discipline | Informatik Mathematik |
| format | Book |
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| genre_facet | Hochschulschrift |
| id | DE-604.BV013643886 |
| illustrated | Illustrated |
| indexdate | 2024-08-20T00:38:14Z |
| institution | BVB |
| isbn | 3826585593 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009321997 |
| oclc_num | 47307105 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-12 DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-91G DE-BY-TUM DE-29T |
| owner_facet | DE-19 DE-BY-UBM DE-12 DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-91G DE-BY-TUM DE-29T |
| physical | X, 253 S. Ill., graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Shaker |
| record_format | marc |
| series2 | Berichte aus der Informatik |
| spelling | Breunig, Markus M. 1971- Verfasser (DE-588)122803299 aut Quality driven database mining Markus M. Breunig Aachen Shaker 2001 X, 253 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Berichte aus der Informatik Zugl.: München, Univ., Fak. für Mathematik und Informatik, Diss., 2001 Hochschulschrift gtt Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Ausreißer Statistik (DE-588)4510494-3 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Hierarchisches Clusterverfahren (DE-588)4238470-9 gnd rswk-swf Wissensextraktion (DE-588)4546354-2 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Wissensextraktion (DE-588)4546354-2 s Data Mining (DE-588)4428654-5 s Cluster-Analyse (DE-588)4070044-6 s Ausreißer Statistik (DE-588)4510494-3 s DE-604 Hierarchisches Clusterverfahren (DE-588)4238470-9 s DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009321997&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Breunig, Markus M. 1971- Quality driven database mining Hochschulschrift gtt Cluster-Analyse (DE-588)4070044-6 gnd Ausreißer Statistik (DE-588)4510494-3 gnd Data Mining (DE-588)4428654-5 gnd Hierarchisches Clusterverfahren (DE-588)4238470-9 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| subject_GND | (DE-588)4070044-6 (DE-588)4510494-3 (DE-588)4428654-5 (DE-588)4238470-9 (DE-588)4546354-2 (DE-588)4113937-9 |
| title | Quality driven database mining |
| title_auth | Quality driven database mining |
| title_exact_search | Quality driven database mining |
| title_full | Quality driven database mining Markus M. Breunig |
| title_fullStr | Quality driven database mining Markus M. Breunig |
| title_full_unstemmed | Quality driven database mining Markus M. Breunig |
| title_short | Quality driven database mining |
| title_sort | quality driven database mining |
| topic | Hochschulschrift gtt Cluster-Analyse (DE-588)4070044-6 gnd Ausreißer Statistik (DE-588)4510494-3 gnd Data Mining (DE-588)4428654-5 gnd Hierarchisches Clusterverfahren (DE-588)4238470-9 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| topic_facet | Hochschulschrift Cluster-Analyse Ausreißer Statistik Data Mining Hierarchisches Clusterverfahren Wissensextraktion |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009321997&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT breunigmarkusm qualitydrivendatabasemining |