Data clustering and beyond: a deterministic annealing framework for exploratory data analysis
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | German |
| Published: |
Aachen
Shaker
1997
|
| Edition: | Als Ms. gedr. |
| Series: | Berichte aus der Informatik
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Zugl.: Aachen, Techn. Hochsch., Diss., 1997 |
| Physical Description: | VI, 238 S. Ill., graph. Darst. |
| ISBN: | 3826531159 |
Staff View
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
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| 001 | BV011604894 | ||
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| 007 | t | ||
| 008 | 971027s1997 gw ad|| m||| 00||| ger d | ||
| 016 | 7 | |a 951975994 |2 DE-101 | |
| 020 | |a 3826531159 |c kart. : DM 98.00, sfr 99.00, S 689.00 |9 3-8265-3115-9 | ||
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| 100 | 1 | |a Hofmann, Thomas |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Data clustering and beyond |b a deterministic annealing framework for exploratory data analysis |c Thomas Hofmann |
| 250 | |a Als Ms. gedr. | ||
| 264 | 1 | |a Aachen |b Shaker |c 1997 | |
| 300 | |a VI, 238 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.: Aachen, Techn. Hochsch., Diss., 1997 | ||
| 650 | 0 | 7 | |a Cluster-Analyse |0 (DE-588)4070044-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Explorative Datenanalyse |0 (DE-588)4128896-8 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Explorative Datenanalyse |0 (DE-588)4128896-8 |D s |
| 689 | 0 | 1 | |a Cluster-Analyse |0 (DE-588)4070044-6 |D s |
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Record in the Search Index
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| adam_text |
CONTENTS
PREFACE
AND
OVERVIEW
VII
I
FUNDAMENTALS
1
1
DATA
ANALYSIS
AND
OPTIMIZATION
3
1.1
DATA
ANALYSIS
.
3
1.1.1
DATA
TAXONOMY
.
3
1.1.2
KEY
PROBLEMS
IN
DATA
ANALYSIS
.
6
1.2
DATA
CLUSTERING
.
7
1.2.1
INTRODUCTION
TO
DATA
CLUSTERING
.
7
1.2.2
GOALS
OF
DATA
CLUSTERING
.
9
1.3
COMBINATORIAL
OPTIMIZATION
.
10
1.3.1
DATA
ANALYSIS
BY
OPTIMIZATION
.
10
1.3.2
PARTITIONING
AND
MATCHING
PROBLEMS
.
10
1.3.3
OPTIMIZATION
METHODS
.
11
1.3.4
OPTIMIZATION
BY
DETERMINISTIC
ANNEALING
.
11
2
DETERMINISTIC
ANNEALING
13
2.1
INTRODUCTION
.
13
2.2
MAXIMUM
ENTROPY
AND
MINIMUM
CROSS-ENTROPY
INFERENCE
.
14
2.2.1
PRELIMINARIES
.
14
2.2.2
MAXIMUM
ENTROPY
INFERENCE
PRINCIPLE
.
16
2.2.3
MINIMUM
CROSS-ENTROPY
INFERENCE
PRINCIPLE
.
17
2.2.4
RATIONALE
OF
ENTROPY
INFERENCE
PRINCIPLES
.
17
2.2.5
FROM
INFERENCE
TO
OPTIMIZATION:
PHYSICAL
SYSTEMS
AND
OPTIMIZATION
.
18
2.3
SIMULATED
ANNEALING
.
18
2.3.1
MARKOV
CHAIN
MONTE
CARLO
.
18
2.3.2
METROPOLIS
SAMPLER
.
20
2.3.3
GIBBS
SAMPLER
.
20
2.3.4
ANNEALING
.
21
2.4
PRINCIPLES
OF
DETERMINISTIC
ANNEALING
.
21
II
CONTENTS
2.4.1
MAXIMUM
ENTROPY
OPTIMIZATION
.
21
2.4.2
ANNEALED
FAMILY
AND
GENERALIZED
FREE
ENERGY
.
22
2.4.3
CONTINUATION
METHODS
.
24
2.4.4
FROM
SIMULATED
TO
DETERMINISTIC
ANNEALING
.
26
2.5
FACTORIAL
GIBBS
DISTRIBUTIONS
AND
MEAN-FIELD
ANNEALING
.
26
2.5.1
FACTORIAL
GIBBS
DISTRIBUTIONS
.
26
2.5.2
MEAN-FIELD
APPROXIMATION
.
30
2.5.3
GIBBS
SAMPLING
AND
MEAN-FIELD
APPROXIMATION
.
34
2.6
DETERMINISTIC
ANNEALING
FOR
DATA
ANALYSIS
.
35
2.6.1
DETERMINISTIC
ANNEALING
FOR
PARTITIONING
PROBLEMS
.
35
2.6.2
DETERMINISTIC
ANNEALING
FOR
MATCHING
PROBLEMS
.
36
2.6.3
DETERMINISTIC
ANNEALING
FOR
COMBINED
PARTITIONING
PROBLEMS
.
38
II
DATA
CLUSTERING
39
3
DATA
CLUSTERING
AND
ROBUST
VECTOR
QUANTIZATION
41
3.1
VECTOR
QUANTIZATION
AND
DATA
CLUSTERING
.
41
3.1.1
INTRODUCTION
.
41
3.1.2
VECTOR
QUANTIZATION
BY
DETERMINISTIC
ANNEALING
.
43
3.2
TOPOLOGY
PRESERVING
MAPS
.
47
3.2.1
SOURCE
CHANNEL
CODING
.
47
3.2.2
TOPOGRAPHIC
MAPS
AND
TOPOLOGICALLY
ORGANIZED
CODEBOOKS
.
48
3.3
ROBUST
VECTOR
QUANTIZATION
.
49
3.3.1
CODE
VECTOR
ELIMINATION
MODEL
.
49
3.3.2
GENERALIZED
R
'
-MEANS
FOR
ROBUST
VECTOR
QUANTIZATION
.
52
3.3.3
ANNEALED
ROBUST
VECTOR
QUANTIZATION
.
52
3.4
LEARNING
DATA
TOPOLOGIES
.
58
3.4.1
THE
TOPNET
MODEL
.
58
3.4.2
ZERO
TEMPERATURE
ALGORITHM
.
60
3.4.3
VARIATIONAL
APPROXIMATION
.
60
3.4.4
MEAN-FIELD
EQUATIONS
.
62
3.4.5
DATA
ANALYSIS
WITH
THE
TOPNET
MODEL
.
63
3.5
COMPLEXITY
CONSTRAINED
VECTOR
QUANTIZATION
.
65
3.6
ON-LINE
EQUATIONS
AND
COMPETITIVE
LEARNING
.
66
3.7
RESULTS
.
68
3.7.1
SYNTHETICAL
DATA
.
68
3.7.2
ROBUST
ENCODING
OF
VIDEO
SEQUENCES
.
71
4
PAIRWISE
DATA
CLUSTERING
81
4.1
INTRODUCTION
.
81
4.2
A
SHORT
TRACK
TO
PAIRWISE
DATA
CLUSTERING
.
82
CONTENTS
III
4.3
AXIOMATIZATION
OF
OBJECTIVE
FUNCTIONS
FOR
DATA
CLUSTERING
.
84
4.3.1
GOALS
OF
AN
AXIOMATIZATION
.
84
4.3.2
FUNDAMENTAL
AXIOMS
FOR
CLUSTERING
PROXIMITY
DATA
.
85
4.3.3
ADDITIVE
CLUSTERING
OBJECTIVE
FUNCTIONS
FOR
COMPLETE
PROXIMITY
DATA
.
89
4.3.4
INVARIANT
INTRA-CLUSTER
COMPACTNESS
MEASURES
.
91
4.3.5
INVARIANT
INTER-CLUSTER
SEPARABILITY
MEASURES
.
93
4.3.6
SUMMARY:
INVARIANT
AND
ROBUST
CLUSTERING
CRITERIA
.
95
4.3.7
EXTENSIONS
FOR
SPARSE
PROXIMITY
DATA
.
96
4.4
COMPARISON
WITH
AGGLOMERATIVE
METHODS
.
99
4.4.1
AGGLOMERATIVE
METHODS
.
99
4.4.2
GLOBAL
AND
LOCAL
OPTIMIZATION
PRINCIPLES
.
100
4.4.3
OTHER
AGGLOMERATIVE
AVERAGING
METHODS
.
102
4.5
GIBBS
SAMPLING
FOR
PAIRWISE
DATA
CLUSTERING
.
102
4.5.1
GENERAL
GIBBS
SAMPLING
EQUATIONS
.
102
4.5.2
INTRA-CLUSTER
COMPACTNESS
MEASURES
.
102
4.5.3
INTER-CLUSTER
SEPARATION
MEASURES
.
104
4.5.4
SPARSE
PROXIMITY
DATA
.
105
4.6
MEAN-FIELD
APPROXIMATION
FOR
PAIRWISE
DATA
CLUSTERING
.
106
4.6.1
INTRODUCTION
.
106
4.6.2
INTRA-CLUSTER
COMPACTNESS
MEASURES
.
106
4.6.3
INTER-CLUSTER
SEPARATION
MEASURES
.
ILL
4.6.4
TAP
EQUATIONS
FOR
PAIRWISE
DATA
CLUSTERING
.
ILL
4.7
RESULTS
.
113
4.7.1
BENCHMARK
EXPERIMENTS
FOR
DETERMINISTIC
ANNEALING
.
114
4.7.2
CLUSTERING
RESULTS
ON
REAL-WORLD
DATA
.
117
APPENDIX
.
120
5
HIERARCHICAL
DATA
CLUSTERING
127
5.1
INTRODUCTION
.
127
5.2
DECISION
TREES
FOR
HIERARCHICAL
DATA
CLUSTERING
.
129
5.2.1
PROBABILISTIC
DECISION
TREE
MODEL
.
129
5.2.2
DECISION
TREES
FOR
HIERARCHICAL
VECTOR
QUANTIZATION
.
131
5.2.3
VARIATIONAL
APPROACH
FOR
HIERARCHICAL
VECTOR
QUANTIZATION
.
133
5.2.4
PARAMETER-FREE
DECISION
TREE
MODEL
.
135
5.2.5
DECISION
TREES
FOR
PROXIMITY
DATA
.
137
5.2.6
LEARNING
THE
TREE
TOPOLOGY
.
140
5.2.7
RESULTS
.
141
5.3
OBJECTIVE
FUNCTIONS
FOR
HIERARCHICAL
DATA
PARTITIONINGS
.
142
5.3.1
HIERARCHICAL
DATA
PARTITIONINGS
.
142
5.3.2
OPTIMIZING
HIERARCHICAL
PARTITIONINGS
FOR
VECTORIAL
DATA
.
145
5.3.3
OPTIMIZING
HIERARCHICAL
PARTITIONS
FOR
PROXIMITY
DATA
.
145
IV
CONTENTS
5.3.4
HIERARCHICAL
OPTIMIZATION
VS.
DECISION
TREE
MODELS
.
147
5.3.5
CLUSTER
VALIDATION
FOR
HIERARCHICAL
OPTIMIZATION
.
149
5.3.6
RESULTS
.
151
III
BEYOND
DATA
CLUSTERING
159
6
STRUCTURE
PRESERVING
DATA
VISUALIZATION
161
6.1
INTRODUCTION
.
161
6.1.1
MULTIDIMENSIONAL
SCALING
.
162
6.1.2
STRUCTURE
PRESERVATION
PRINCIPLE
.
163
6.2
CLUSTER
PRESERVING
EUCLIDEAN
MDS
.
164
6.2.1
APPROXIMATION
OF
PAIRWISE
CLUSTERING
BY
VECTORIAL
CLUSTERING
.
164
6.2.2
STATIONARY
EQUATIONS
AND
ALGORITHMIC
QUESTIONS
.
165
6.2.3
CLUSTERING
AND
MULTIDIMENSIONAL
SCALING
.
167
6.2.4
DIMENSIONALITY
REDUCTION
.
170
6.3
EXTENSIONS
.
171
6.3.1
GENERALIZED
EMBEDDING
METRICS
.
171
6.3.2
SPHERICAL
MDS
.
172
6.3.3
HIERARCHICAL
PRESERVATION
PRINCIPLE
.
172
6.4
RESULTS
.
172
7
ACTIVE
DATA
ANALYSIS
179
7.1
INTRODUCTION
.
179
7.2
INFORMATION
MAXIMIZING
QUERIES
.
180
7.2.1
INFORMATION
GAIN
.
180
7.2.2
QUENCHED
AVERAGES
.
181
7.2.3
ACTIVE
QUERYING
STRATEGIES
.
183
7.3
ACTIVE
DATA
CLUSTERING
.
183
7.3.1
A
SIMPLE
LINEAR
ASSIGNMENT
PROBLEM
.
183
7.3.2
ACTIVE
PAIRWISE
DATA
CLUSTERING
.
190
7.4
RESULTS
.
193
7.4.1
SYNTHETICAL
PROXIMITY
DATA
.
193
7.4.2
REAL-WORLD
DATA
.
194
8
UNSUPERVISED
SEGMENTATION
OF
TEXTURED
IMAGES
197
8.1
INTRODUCTION
.
197
8.2
IMAGE
REPRESENTATION
AND
PROXIMITY
EVALUATION
.
198
8.3
CLUSTERING
FOR
TEXTURE
SEGMENTATION
.
201
8.4
RESULTS
.
202
8.4.1
TEXTURE
SEGMENTATION
BY
SPARSE
CLUSTERING
.
202
8.4.2
MEAN-FIELD
APPROXIMATION
AND
GIBBS
SAMPLING
.
208
CONTENTS
V
8.4.3
REAL-WORLD
IMAGES
.
211
9
CONCLUSION
215
A
REAL-WORLD
DATA
SETS
217
A.L
PROTEIN
SEQUENCE
DATA
.
217
A.
2
LINGUISTIC
DATA
.
218
A.
3
VIDEO
SEQUENCE
DATA
.
218
A.4
SATELLITE
DATA
.
218
A.
5
TEXTURE
DATA
.
219 |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV011604894 |
| illustrated | Illustrated |
| indexdate | 2024-08-14T00:20:47Z |
| institution | BVB |
| isbn | 3826531159 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007817978 |
| oclc_num | 64545904 |
| open_access_boolean | |
| owner | DE-739 DE-706 |
| owner_facet | DE-739 DE-706 |
| physical | VI, 238 S. Ill., graph. Darst. |
| publishDate | 1997 |
| publishDateSearch | 1997 |
| publishDateSort | 1997 |
| publisher | Shaker |
| record_format | marc |
| series2 | Berichte aus der Informatik |
| spelling | Hofmann, Thomas Verfasser aut Data clustering and beyond a deterministic annealing framework for exploratory data analysis Thomas Hofmann Als Ms. gedr. Aachen Shaker 1997 VI, 238 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Berichte aus der Informatik Zugl.: Aachen, Techn. Hochsch., Diss., 1997 Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Explorative Datenanalyse (DE-588)4128896-8 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Explorative Datenanalyse (DE-588)4128896-8 s Cluster-Analyse (DE-588)4070044-6 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007817978&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hofmann, Thomas Data clustering and beyond a deterministic annealing framework for exploratory data analysis Cluster-Analyse (DE-588)4070044-6 gnd Explorative Datenanalyse (DE-588)4128896-8 gnd |
| subject_GND | (DE-588)4070044-6 (DE-588)4128896-8 (DE-588)4113937-9 |
| title | Data clustering and beyond a deterministic annealing framework for exploratory data analysis |
| title_auth | Data clustering and beyond a deterministic annealing framework for exploratory data analysis |
| title_exact_search | Data clustering and beyond a deterministic annealing framework for exploratory data analysis |
| title_full | Data clustering and beyond a deterministic annealing framework for exploratory data analysis Thomas Hofmann |
| title_fullStr | Data clustering and beyond a deterministic annealing framework for exploratory data analysis Thomas Hofmann |
| title_full_unstemmed | Data clustering and beyond a deterministic annealing framework for exploratory data analysis Thomas Hofmann |
| title_short | Data clustering and beyond |
| title_sort | data clustering and beyond a deterministic annealing framework for exploratory data analysis |
| title_sub | a deterministic annealing framework for exploratory data analysis |
| topic | Cluster-Analyse (DE-588)4070044-6 gnd Explorative Datenanalyse (DE-588)4128896-8 gnd |
| topic_facet | Cluster-Analyse Explorative Datenanalyse Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007817978&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hofmannthomas dataclusteringandbeyondadeterministicannealingframeworkforexploratorydataanalysis |