Visual data mining:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
dissertation.de
2001
|
| Ausgabe: | Als Ms. gedr. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zugl.: München, Univ., Fak. für Mathematik und Informatik, Diss., 2000 |
| Beschreibung: | VI, 216 S. Ill., graph. Darst. |
| ISBN: | 3898252019 |
Internformat
MARC
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| 008 | 010109s2001 gw ad|| m||| 00||| eng d | ||
| 016 | 7 | |a 960433058 |2 DE-101 | |
| 020 | |a 3898252019 |c Pb. : DM 82.00 |9 3-89825-201-9 | ||
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| 100 | 1 | |a Ankerst, Mihael |d 1972- |e Verfasser |0 (DE-588)122579925 |4 aut | |
| 245 | 1 | 0 | |a Visual data mining |c Mihael Ankerst |
| 250 | |a Als Ms. gedr. | ||
| 264 | 1 | |a Berlin |b dissertation.de |c 2001 | |
| 300 | |a VI, 216 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Zugl.: München, Univ., Fak. für Mathematik und Informatik, Diss., 2000 | ||
| 650 | 7 | |a Hochschulschrift |2 gtt | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Database searching | |
| 650 | 0 | 7 | |a Visualisierung |0 (DE-588)4188417-6 |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 Wissensextraktion |0 (DE-588)4546354-2 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Visualisierung |0 (DE-588)4188417-6 |D s |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-009236289 | |
Datensatz im Suchindex
| _version_ | 1807683176604106752 |
|---|---|
| adam_text |
III
TABLE
OF
CONTENTS
1
INTRODUCTION
1
1.1
THE
KDD
PROCESS
.
1
1.2
WHAT
IS
VISUAL
DATA
MINING
?
.
5
1.3
TERMINOLOGY
AND
OUTLINE
OF
THE
THESIS
.
10
2
RELATED
WORK
13
2.1
DATA
MINING
.
13
2.1.1
ASSOCIATION
RULES
.
14
2.1.2
GENERALIZATION
.
15
2.2
DECISION
TREE
CLASSIFICATION
.
16
2.2.1
CONSTRUCTING
A
DECISION
TREE
.
17
2.2.2
PRUNING
.
20
2.2.3
OTHER
ISSUES
.
22
2.3
CLUSTERING
.
25
2.3.1
HIERARCHICAL
ALGORITHMS
.
25
2.3.2
PARTITIONING
ALGORITHMS
.
26
2.4
VISUALIZATION
.
28
2.4.
1
VISUALIZATION
TECHNIQUES
FOR
EXPLORATIVE
ANALYSIS
.
29
2.4.2
VISUAL
DATA
MINING
.
32
PARTI
37
3
VISUAL
CLASSIFICATION
39
3.1
INTRODUCTION
.
39
3.2
RELATED
WORK
.
42
3.2.1
DECISION
TREE
CONSTRUCTION
AND
PRUNING
.
42
3.2.2
STATE-OF-THE-ART
DECISION
TREE
ALGORITHMS
.
44
3.3
VISUALIZING
THE
DATA
.
45
3.3.1
THE
CIRCLE
SEGMENTS
VISUALIZATION
TECHNIQUE
.
46
3.3.2
VISUALIZING
THE
CLASSES
.
48
3.3.3
NUMERICAL
DATA
.
51
3.3.4
CATEGORICAL
DATA
.
52
3.3.5
SHUFFLING
.
54
3.4
INTERACTION
FACILITIES
.
55
3.4.1
A
MODEL
FOR
INTERACTIVE
CLASSIFICATION
.
56
3.4.2
THE
TWO-STEP
SELECTION
OF
THE
SPLIT
POINT
.
57
3.5
A
STRATEGY
FOR
INTERACTIVE
DECISION
TREE
CONSTRUCTION
.
59
3.5.1
BEST
PURE
PARTITIONS
.
60
3.5.2
LARGEST
CLUSTER
PARTITIONING
.
61
3.5.3
BEST
COMPLETE
PARTITIONING
.
62
IV
3.5.4
DIFFERENT
DISTRIBUTION
PARTITIONING
.
63
3.5.5
INCLUDING
DOMAIN
KNOWLEDGE
.
64
3.6
EXPERIMENTS
.
65
3.6.1
THE
PRINCIPLE
OF
INTERACTIVE
CLASSIFICATION
.
66
3.6.2
A
SAMPLE
DECISION
TREE
.
68
3.7
CONCLUSION
.
68
4
COOPERATIVE
CLASSIFICATION
71
4.1
INTRODUCTION
.
71
4.2
A
NEW
VISUALIZATION
OF
THE
DATA
AND
THE
KNOWLEDGE
.
73
4.2.1
BAR
VISUALIZATION
OF
THE
DATA
.
73
4.2.2
A
NEW
TECHNIQUE
FOR
KNOWLEDGE
VISUALIZATION
.
74
4.3
INTEGRATING
ALGORITHMS
INTO
COOPERATIVE
DECISION
TREE
CONSTRUCTION
.
77
4.3.1
ALGORITHMIC
SUPPORT
FOR
THE
USER
.
77
4.3.2
A
MODEL
FOR
COOPERATIVE
DECISION
TREE
CONSTRUCTION
.
82
4.4
EXPERIMENTAL
EVALUATION
.
85
4.4.1
A
DISCUSSION
OF
N-ARY
AND
BINARY
TREES
.
86
4.4.2
TEST
ENVIRONMENT
.
88
4.4.3
COMPARISON
WITH
STATE-OF-THE-ART
ALGORITHMS
.
89
4.4.4
COMPARISON
OF
DIFFERENT
STYLES
OF
COOPERATION
.
91
4.5
CONCLUSION
.
95
5
FURTHER
IMPROVEMENTS
OF
VISUAL
CLASSIFICATION
97
5.1
CONSIDERING
THE
PROPERTIES
OF
THE
CLASS
ATTRIBUTE
.
97
5.1.1
INCORPORATING
THE
ORDER
OF
THE
CLASS
ATTRIBUTE
.
98
5.1.2
IMPRECISE
CLASS
AND
COST
DISTRIBUTIONS
.
100
5.2
VISUALIZING
THE
ATTRIBUTE
VALUES
WITH
RESPECT
TO
THE
CLASS
LABEL
.
104
5.2.1
A
NEW
SPLITTING
CRITERION
.
105
5.2.2
DISCRETIZING
CONTINUOUS
ATTRIBUTES
.
106
5.2.3
THE
ATTRIBUTE
PLOT
.
108
5.3
PBC
-
THE
SYSTEM
ARCHITECTURE
.
110
5.3.1
THE
SYSTEM
ARCHITECTURE
.
ILL
5.3.2
VISUALIZING
THE
TRAINING
DATA
.
113
5.4
CONCLUSION
.
115
PART
II
117
6
VISUALIZING
THE
CLUSTERING
STRUCTURE
OF
HIGH-DIMENSIONAL
DATA
119
6.1
INTRODUCTION
.
120
6.2
RELATED
WORK
.
122
6.3
ORDERING
THE
DATABASE
WITH
RESPECT
TO
THE
CLUSTERING
STRUCTURE
.
124
6.3.1
MOTIVATION
.
124
6.3.2
DENSITIY-BASED
CLUSTERING
.
125
6.3.3
DENSITY-BASED
CLUSTER-ORDERING
.
127
6.3.4
OPTICS
ALGORITHMS
.
131
V
6.4
VISUALIZING
THE
CLUSTERING
STRUCTURE
.
136
6.4.1
PARAMETERS
AND
REACHABILITY-PLOTS
FOR
MEDIUM
SIZED
DATA
SETS
.
136
6.4.2
VISUALIZATION
OF
LARGE
HIGH-DIMENSIONAL
DATA
SETS
.
141
6.5
CONCLUSION
.
144
7
DATA
TUBE:
A
NEW
VISUALIZATION
TECHNIQUE
FOR
KDD
147
7.1
INTRODUCTION
.
148
7.2
RELATED
WORK
.
149
7.2.1
GENERAL
APPROACHES
.
149
7.2.2
LIMITATIONS
OF
PIXEL-ORIENTED
TECHNIQUES
.
150
7.3
THE
CONCEPT
OF
DATA
TUBE
.
154
7.3.1
THE
REQUIREMENTS
OF
A
KDD
VISUALIZATION
TECHNIQUE
.
154
7.3.2
THE
DATA
TUBE
APPROACH
.
156
7.3.3
VISUAL
DATA
MINING
WITH
DATA
TUBE
.
158
7.4
APPLICATIONS
OF
THE
DATA
TUBE
TECHNIQUE
.
162
7.5
CONCLUSION
.
167
8
SIMILARITY
CLUSTERING
OF
ATTRIBUTES
FOR
ENHANCED
VISUALIZATIONS
169
8.1
INTRODUCTION
.
170
8.2
SIMILARITY
OF
ATTRIBUTES
.
171
8.2.1
GLOBAL
SIMILARITY
OF
TWO
ATTRIBUTES
.
175
8.2.2
PARTIAL
SIMILARITY
OF
TWO
ATTRIBUTES
.
176
8.2.3
EFFICIENCY
CONSIDERATIONS
.
177
8.3
SIMILARITY
ARRANGEMENT
OF
THE
ATTRIBUTES
.
179
8.3.1
DEFINITION
OF
THE
ARRANGEMENT
PROBLEMS
.
179
8.3.2
COMPLEXITY
OF
THE
ARRANGEMENT
PROBLEMS
.
183
8.4
THE
ATTRIBUTE
ARRANGEMENT
.
189
8.5
EXPERIMENTAL
EVALUATION
.
191
8.6
CONCLUSION
.
196
9
CONCLUSIONS
199 |
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| discipline | Informatik |
| edition | Als Ms. gedr. |
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| id | DE-604.BV013529855 |
| illustrated | Illustrated |
| indexdate | 2024-08-18T00:30:17Z |
| institution | BVB |
| isbn | 3898252019 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009236289 |
| oclc_num | 47313507 |
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| owner_facet | DE-12 DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-29T DE-703 |
| physical | VI, 216 S. Ill., graph. Darst. |
| publishDate | 2001 |
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| publisher | dissertation.de |
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| spelling | Ankerst, Mihael 1972- Verfasser (DE-588)122579925 aut Visual data mining Mihael Ankerst Als Ms. gedr. Berlin dissertation.de 2001 VI, 216 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Zugl.: München, Univ., Fak. für Mathematik und Informatik, Diss., 2000 Hochschulschrift gtt Data mining Database searching Visualisierung (DE-588)4188417-6 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Wissensextraktion (DE-588)4546354-2 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Data Mining (DE-588)4428654-5 s Visualisierung (DE-588)4188417-6 s DE-604 Wissensextraktion (DE-588)4546354-2 s DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009236289&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ankerst, Mihael 1972- Visual data mining Hochschulschrift gtt Data mining Database searching Visualisierung (DE-588)4188417-6 gnd Data Mining (DE-588)4428654-5 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| subject_GND | (DE-588)4188417-6 (DE-588)4428654-5 (DE-588)4546354-2 (DE-588)4113937-9 |
| title | Visual data mining |
| title_auth | Visual data mining |
| title_exact_search | Visual data mining |
| title_full | Visual data mining Mihael Ankerst |
| title_fullStr | Visual data mining Mihael Ankerst |
| title_full_unstemmed | Visual data mining Mihael Ankerst |
| title_short | Visual data mining |
| title_sort | visual data mining |
| topic | Hochschulschrift gtt Data mining Database searching Visualisierung (DE-588)4188417-6 gnd Data Mining (DE-588)4428654-5 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| topic_facet | Hochschulschrift Data mining Database searching Visualisierung Data Mining Wissensextraktion |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009236289&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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