Cluster analysis for data mining and system identification:
"This book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets. Special attentio...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Basel [u.a.]
Birkhäuser
2007
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Zusammenfassung: | "This book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets. Special attention is given to the analysis of historical process data, tailored algorithms are presented for the data driven modeling of dynamical systems, determining the model order of nonlinear input-output black box models, and the segmentation of multivariate time-series. The main methods and techniques are illustrated through several simulated and real-world applications from data mining and process engineering practice." -- Book cover. |
| Beschreibung: | XVIII, 303 S. Ill., graph. Darst. |
| ISBN: | 9783764379872 3764379871 |
Internformat
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| 300 | |a XVIII, 303 S. |b Ill., graph. Darst. | ||
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| 520 | 3 | |a "This book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets. Special attention is given to the analysis of historical process data, tailored algorithms are presented for the data driven modeling of dynamical systems, determining the model order of nonlinear input-output black box models, and the segmentation of multivariate time-series. The main methods and techniques are illustrated through several simulated and real-world applications from data mining and process engineering practice." -- Book cover. | |
| 650 | 4 | |a Cluster analysis | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a System identification | |
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Datensatz im Suchindex
| _version_ | 1805088643948216320 |
|---|---|
| adam_text |
CLUSTER ANALYSIS FOR
DATA MINING AND
SYSTEM IDENTIFICATION
JAENOS ABONYI
BALAEZS FEIL
BIRKHAEUSER
BASEL YY BOSTON YY BERLIN
CONTENT
S
PREFACE IX
1 CLASSICAL FUZZY CLUSTER ANALYSIS
1.1 MOTIVATIO
N 1
1.2 TYPE
S OF DAT
A 4
1.3 SIMILARIT
Y MEASURE
S 5
1.4 CLUSTERIN
G TECHNIQUES 8
1.4.1 HIERARCHICA
L CLUSTERIN
G ALGORITHM
S 9
1.4.2 PARTITIONA
L ALGORITHM
S 10
1.5 FUZZY CLUSTERIN
G 17
1.5.1 FUZZY PARTITIO
N 17
1.5.2 TH
E FUZZY C-MEANS FUNCTIONA
L 18
1.5.3 WAYS FOR REALIZING FUZZY CLUSTERIN
G 18
1.5.4 TH
E FUZZY C-MEANS ALGORITH
M 19
1.5.5 INNER-PRODUC
T NORM
S 24
1.5.6 GUSTAFSON-KESSE
L ALGORITH
M 24
1.5.7 GATH-GEV
A CLUSTERIN
G ALGORITH
M 28
1.6 CLUSTE
R ANALYSIS OF CORRELATE
D DAT
A 32
1.7 VALIDITY MEASURE
S 40
2 VISUALIZATION OF THE CLUSTERING RESULTS
2.1 INTRODUCTION
: MOTIVATIO
N AN
D METHOD
S 47
2.1.1 PRINCIPA
L COMPONEN
T ANALYSIS 48
2.1.2 SAMMO
N MAPPIN
G 52
2.1.3 KOHONE
N SELF-ORGANIZING MAP
S 54
2.2 FUZZY SAMMO
N MAPPIN
G 59
2.2.1 MODIFIED SAMMO
N MAPPIN
G 60
2.2.2 APPLICATIO
N EXAMPLE
S 61
2.2.3 CONCLUSIONS 66
2.3 FUZZY SELF-ORGANIZING MA
P 67
2.3.1 REGULARIZED FUZZY C-MEANS CLUSTERIN
G 68
2.3.2 CASE STUD
Y 75
2.3.3 CONCLUSIONS 79
CONTENT
S
CLUSTERING FOR FUZZY MODEL IDENTIFICATION - REGRESSION
3.1 INTRODUCTIO
N T
O FUZZY MODELLING 81
3.2 TAKAGI-SUGEN
O (TS
) FUZZY MODEL
S 86
3.2.1 STRUCTUR
E OF ZERO-AN
D FIRST-ORDE
R T
S FUZZY MODELS .
. 87
3.2.2 RELATE
D MODELLING PARADIGM
S 92
3.3 T
S FUZZY MODELS FOR NONLINEA
R REGRESSION 96
3.3.1 FUZZY MODEL IDENTIFICATION BASED ON
GATH-GEV
A CLUSTERIN
G 98
3.3.2 CONSTRUCTIO
N OF ANTECEDEN
T MEMBERSHI
P FUNCTION
S 100
3.3.3 MODIFIED GATH-GEV
A CLUSTERIN
G 102
3.3.4 SELECTION OF TH
E ANTECEDEN
T AN
D CONSEQUEN
T VARIABLES . . . 111
3.3.5 CONCLUSIONS 115
3.4 FUZZY REGRESSION TREE 115
3.4.1 PRELIMINARIE
S 120
3.4.2 IDENTIFICATIO
N OF FUZZY REGRESSION TREES BASE
D
ON CLUSTERIN
G ALGORITH
M 122
3.4.3 CONCLUSIONS 133
3.5 CLUSTERIN
G FOR STRUCTUR
E SELECTION 133
3.5.1 INTRODUCTIO
N 133
3.5.2 INPU
T SELECTION FOR DISCRETE DAT
A 134
3.5.3 FUZZY CLUSTERIN
G APPROAC
H T
O INPU
T SELECTION 136
3.5.4 EXAMPLE
S 137
3.5.5 CONCLUSIONS 139
FUZZY CLUSTERING FOR SYSTEM IDENTIFICATION
4.1 DATA-DRIVE
N MODELLING OF DYNAMICAL SYSTEMS 142
4.1.1 T
S FUZZY MODELS OF SISO AN
D MIM
O SYSTEM
S 148
4.1.2 CLUSTERIN
G FOR TH
E IDENTIFICATION OF MIMO PROCESSE
S .
. 153
4.1.3 CONCLUSION
S 161
4.2 SEMI-MECHANISTI
C FUZZY MODELS 162
4.2.1 INTRODUCTIO
N T
O SEMI-MECHANISTIC MODELLING 162
4.2.2 STRUCTUR
E OF TH
E SEMI-MECHANISTIC FUZZY MODEL 164
4.2.3 CLUSTERING-BASE
D IDENTIFICATION OF TH
E
SEMI-MECHANISTI
C FUZZY MODEL 171
4.2.4 CONCLUSION
S 182
4.3 MODEL ORDE
R SELECTION 183
4.3.1 INTRODUCTIO
N 183
4.3.2 FN
N ALGORITH
M 185
4.3.3 FUZZY CLUSTERIN
G BASED FNN 187
4.3.4 CLUSTE
R ANALYSI
S BASED DIRECT MODEL ORDE
R ESTIMATIO
N . . . 189
4.3.5 APPLICATIO
N EXAMPLE
S 190
4.3.6 CONCLUSION
S 198
4.4 STATE-SPAC
E RECONSTRUCTIO
N 198
4.4.1 INTRODUCTIO
N 198
CONTENTS VII
4.4.2 CLUSTERING-BASE
D APPROAC
H T
O
STATE-SPAC
E RECONSTRUCTIO
N 200
4.4.3 APPLICATIO
N EXAMPLE
S AN
D DISCUSSION 208
4.4.4 CASE STUD
Y 216
4.4.5 CONCLUSIONS 222
5 FUZZY MODEL BASED CLASSIFIERS
5.1 FUZZY MODEL STRUCTURE
S FOR CLASSIFICATION 227
5.1.1 CLASSICAL BAYES CLASSINER 227
5.1.2 CLASSICAL FUZZY CLASSINER 228
5.1.3 BAYES CLASSIFIER BASE
D ON MIXTUR
E OF DENSIT
Y MODELS .
. 229
5.1.4 EXTENDE
D FUZZY CLASSIFIER 229
5.1.5 FUZZY DECISION TREE FOR CLASSIFICATION 230
5.2 ITERATIV
E LEARNIN
G OF FUZZY CLASSIFIERS 232
5.2.1 ENSURIN
G TRANSPARENC
Y AN
D ACCURAC
Y 233
5.2.2 CONCLUSIONS 237
5.3 SUPERVISED FUZZY CLUSTERIN
G 237
5.3.1 SUPERVISED FUZZY CLUSTERIN
G - TH
E ALGORITH
M 239
5.3.2 PERFORMANC
E EVALUATIO
N 240
5.3.3 CONCLUSIONS 244
5.4 FUZZY CLASSIFICATION TREE 245
5.4.1 FUZZY DECISION TREE INDUCTIO
N 247
5.4.2 TRANSFORMATIO
N AN
D MERGING OF TH
E
MEMBERSHI
P FUNCTION
S 248
5.4.3 CONCLUSIONS 252
6 SEGMENTATION OF MULTIVARIATE TIME-SERIES
6.1 MINING TIME-SERIES DAT
A 253
6.2 TIME-SERIES SEGMENTATIO
N 255
6.3 FUZZY CLUSTE
R BASE
D FUZZY SEGMENTATIO
N 261
6.3.1 PC
A BASE
D DISTANC
E MEASUR
E 263
6.3.2 MODIFIED GATH-GEV
A CLUSTERIN
G FOR
TIME-SERIES SEGMENTATIO
N 264
6.3.3 AUTOMATI
C DETERMINATIO
N OF TH
E NUMBE
R OF SEGMENTS . . . 266
6.3.4 NUMBE
R OF PRINCIPA
L COMPONENT
S 268
6.3.5 TH
E SEGMENTATIO
N ALGORITH
M 269
6.3.6 CASE STUDIE
S 270
6.4 CONCLUSIONS 273
APPENDIX: HERMITE SPLINE INTERPOLATION 275
BIBLIOGRAPHY 279
INDEX 301 |
| adam_txt |
CLUSTER ANALYSIS FOR
DATA MINING AND
SYSTEM IDENTIFICATION
JAENOS ABONYI
BALAEZS FEIL
BIRKHAEUSER
BASEL YY BOSTON YY BERLIN
CONTENT
S
PREFACE IX
1 CLASSICAL FUZZY CLUSTER ANALYSIS
1.1 MOTIVATIO
N 1
1.2 TYPE
S OF DAT
A 4
1.3 SIMILARIT
Y MEASURE
S 5
1.4 CLUSTERIN
G TECHNIQUES 8
1.4.1 HIERARCHICA
L CLUSTERIN
G ALGORITHM
S 9
1.4.2 PARTITIONA
L ALGORITHM
S 10
1.5 FUZZY CLUSTERIN
G 17
1.5.1 FUZZY PARTITIO
N 17
1.5.2 TH
E FUZZY C-MEANS FUNCTIONA
L 18
1.5.3 WAYS FOR REALIZING FUZZY CLUSTERIN
G 18
1.5.4 TH
E FUZZY C-MEANS ALGORITH
M 19
1.5.5 INNER-PRODUC
T NORM
S 24
1.5.6 GUSTAFSON-KESSE
L ALGORITH
M 24
1.5.7 GATH-GEV
A CLUSTERIN
G ALGORITH
M 28
1.6 CLUSTE
R ANALYSIS OF CORRELATE
D DAT
A 32
1.7 VALIDITY MEASURE
S 40
2 VISUALIZATION OF THE CLUSTERING RESULTS
2.1 INTRODUCTION
: MOTIVATIO
N AN
D METHOD
S 47
2.1.1 PRINCIPA
L COMPONEN
T ANALYSIS 48
2.1.2 SAMMO
N MAPPIN
G 52
2.1.3 KOHONE
N SELF-ORGANIZING MAP
S 54
2.2 FUZZY SAMMO
N MAPPIN
G 59
2.2.1 MODIFIED SAMMO
N MAPPIN
G 60
2.2.2 APPLICATIO
N EXAMPLE
S 61
2.2.3 CONCLUSIONS 66
2.3 FUZZY SELF-ORGANIZING MA
P 67
2.3.1 REGULARIZED FUZZY C-MEANS CLUSTERIN
G 68
2.3.2 CASE STUD
Y 75
2.3.3 CONCLUSIONS 79
CONTENT
S
CLUSTERING FOR FUZZY MODEL IDENTIFICATION - REGRESSION
3.1 INTRODUCTIO
N T
O FUZZY MODELLING 81
3.2 TAKAGI-SUGEN
O (TS
) FUZZY MODEL
S 86
3.2.1 STRUCTUR
E OF ZERO-AN
D FIRST-ORDE
R T
S FUZZY MODELS .
. 87
3.2.2 RELATE
D MODELLING PARADIGM
S 92
3.3 T
S FUZZY MODELS FOR NONLINEA
R REGRESSION 96
3.3.1 FUZZY MODEL IDENTIFICATION BASED ON
GATH-GEV
A CLUSTERIN
G 98
3.3.2 CONSTRUCTIO
N OF ANTECEDEN
T MEMBERSHI
P FUNCTION
S 100
3.3.3 MODIFIED GATH-GEV
A CLUSTERIN
G 102
3.3.4 SELECTION OF TH
E ANTECEDEN
T AN
D CONSEQUEN
T VARIABLES . . . 111
3.3.5 CONCLUSIONS 115
3.4 FUZZY REGRESSION TREE 115
3.4.1 PRELIMINARIE
S 120
3.4.2 IDENTIFICATIO
N OF FUZZY REGRESSION TREES BASE
D
ON CLUSTERIN
G ALGORITH
M 122
3.4.3 CONCLUSIONS 133
3.5 CLUSTERIN
G FOR STRUCTUR
E SELECTION 133
3.5.1 INTRODUCTIO
N 133
3.5.2 INPU
T SELECTION FOR DISCRETE DAT
A 134
3.5.3 FUZZY CLUSTERIN
G APPROAC
H T
O INPU
T SELECTION 136
3.5.4 EXAMPLE
S 137
3.5.5 CONCLUSIONS 139
FUZZY CLUSTERING FOR SYSTEM IDENTIFICATION
4.1 DATA-DRIVE
N MODELLING OF DYNAMICAL SYSTEMS 142
4.1.1 T
S FUZZY MODELS OF SISO AN
D MIM
O SYSTEM
S 148
4.1.2 CLUSTERIN
G FOR TH
E IDENTIFICATION OF MIMO PROCESSE
S .
. 153
4.1.3 CONCLUSION
S 161
4.2 SEMI-MECHANISTI
C FUZZY MODELS 162
4.2.1 INTRODUCTIO
N T
O SEMI-MECHANISTIC MODELLING 162
4.2.2 STRUCTUR
E OF TH
E SEMI-MECHANISTIC FUZZY MODEL 164
4.2.3 CLUSTERING-BASE
D IDENTIFICATION OF TH
E
SEMI-MECHANISTI
C FUZZY MODEL 171
4.2.4 CONCLUSION
S 182
4.3 MODEL ORDE
R SELECTION 183
4.3.1 INTRODUCTIO
N 183
4.3.2 FN
N ALGORITH
M 185
4.3.3 FUZZY CLUSTERIN
G BASED FNN 187
4.3.4 CLUSTE
R ANALYSI
S BASED DIRECT MODEL ORDE
R ESTIMATIO
N . . . 189
4.3.5 APPLICATIO
N EXAMPLE
S 190
4.3.6 CONCLUSION
S 198
4.4 STATE-SPAC
E RECONSTRUCTIO
N 198
4.4.1 INTRODUCTIO
N 198
CONTENTS VII
4.4.2 CLUSTERING-BASE
D APPROAC
H T
O
STATE-SPAC
E RECONSTRUCTIO
N 200
4.4.3 APPLICATIO
N EXAMPLE
S AN
D DISCUSSION 208
4.4.4 CASE STUD
Y 216
4.4.5 CONCLUSIONS 222
5 FUZZY MODEL BASED CLASSIFIERS
5.1 FUZZY MODEL STRUCTURE
S FOR CLASSIFICATION 227
5.1.1 CLASSICAL BAYES CLASSINER 227
5.1.2 CLASSICAL FUZZY CLASSINER 228
5.1.3 BAYES CLASSIFIER BASE
D ON MIXTUR
E OF DENSIT
Y MODELS .
. 229
5.1.4 EXTENDE
D FUZZY CLASSIFIER 229
5.1.5 FUZZY DECISION TREE FOR CLASSIFICATION 230
5.2 ITERATIV
E LEARNIN
G OF FUZZY CLASSIFIERS 232
5.2.1 ENSURIN
G TRANSPARENC
Y AN
D ACCURAC
Y 233
5.2.2 CONCLUSIONS 237
5.3 SUPERVISED FUZZY CLUSTERIN
G 237
5.3.1 SUPERVISED FUZZY CLUSTERIN
G - TH
E ALGORITH
M 239
5.3.2 PERFORMANC
E EVALUATIO
N 240
5.3.3 CONCLUSIONS 244
5.4 FUZZY CLASSIFICATION TREE 245
5.4.1 FUZZY DECISION TREE INDUCTIO
N 247
5.4.2 TRANSFORMATIO
N AN
D MERGING OF TH
E
MEMBERSHI
P FUNCTION
S 248
5.4.3 CONCLUSIONS 252
6 SEGMENTATION OF MULTIVARIATE TIME-SERIES
6.1 MINING TIME-SERIES DAT
A 253
6.2 TIME-SERIES SEGMENTATIO
N 255
6.3 FUZZY CLUSTE
R BASE
D FUZZY SEGMENTATIO
N 261
6.3.1 PC
A BASE
D DISTANC
E MEASUR
E 263
6.3.2 MODIFIED GATH-GEV
A CLUSTERIN
G FOR
TIME-SERIES SEGMENTATIO
N 264
6.3.3 AUTOMATI
C DETERMINATIO
N OF TH
E NUMBE
R OF SEGMENTS . . . 266
6.3.4 NUMBE
R OF PRINCIPA
L COMPONENT
S 268
6.3.5 TH
E SEGMENTATIO
N ALGORITH
M 269
6.3.6 CASE STUDIE
S 270
6.4 CONCLUSIONS 273
APPENDIX: HERMITE SPLINE INTERPOLATION 275
BIBLIOGRAPHY 279
INDEX 301 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Abonyi, János 1974- |
| author_GND | (DE-588)133119246 |
| author_facet | Abonyi, János 1974- |
| author_role | aut |
| author_sort | Abonyi, János 1974- |
| author_variant | j a ja |
| building | Verbundindex |
| bvnumber | BV022240960 |
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| callnumber-sort | QA 276.9 D343 |
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| classification_rvk | QH 500 |
| classification_tum | DAT 777f |
| ctrlnum | (OCoLC)144216706 (DE-599)BVBBV022240960 |
| dewey-full | 006.3/12 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/12 |
| dewey-search | 006.3/12 |
| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV022240960 |
| illustrated | Illustrated |
| index_date | 2024-07-02T16:35:58Z |
| indexdate | 2024-07-20T09:11:17Z |
| institution | BVB |
| isbn | 9783764379872 3764379871 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015451881 |
| oclc_num | 144216706 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-473 DE-BY-UBG DE-945 |
| owner_facet | DE-91G DE-BY-TUM DE-473 DE-BY-UBG DE-945 |
| physical | XVIII, 303 S. Ill., graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Birkhäuser |
| record_format | marc |
| spelling | Abonyi, János 1974- Verfasser (DE-588)133119246 aut Cluster analysis for data mining and system identification János Abonyi ; Balázs Feil Basel [u.a.] Birkhäuser 2007 XVIII, 303 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier "This book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets. Special attention is given to the analysis of historical process data, tailored algorithms are presented for the data driven modeling of dynamical systems, determining the model order of nonlinear input-output black box models, and the segmentation of multivariate time-series. The main methods and techniques are illustrated through several simulated and real-world applications from data mining and process engineering practice." -- Book cover. Cluster analysis Data mining System identification Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Fuzzy-Clusteranalyse (DE-588)4802783-2 gnd rswk-swf Fuzzy-Clusteranalyse (DE-588)4802783-2 s Data Mining (DE-588)4428654-5 s DE-604 Systemidentifikation (DE-588)4121753-6 s Feil, Balazs Sonstige oth text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2848634&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=015451881&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Abonyi, János 1974- Cluster analysis for data mining and system identification Cluster analysis Data mining System identification Systemidentifikation (DE-588)4121753-6 gnd Data Mining (DE-588)4428654-5 gnd Fuzzy-Clusteranalyse (DE-588)4802783-2 gnd |
| subject_GND | (DE-588)4121753-6 (DE-588)4428654-5 (DE-588)4802783-2 |
| title | Cluster analysis for data mining and system identification |
| title_auth | Cluster analysis for data mining and system identification |
| title_exact_search | Cluster analysis for data mining and system identification |
| title_exact_search_txtP | Cluster analysis for data mining and system identification |
| title_full | Cluster analysis for data mining and system identification János Abonyi ; Balázs Feil |
| title_fullStr | Cluster analysis for data mining and system identification János Abonyi ; Balázs Feil |
| title_full_unstemmed | Cluster analysis for data mining and system identification János Abonyi ; Balázs Feil |
| title_short | Cluster analysis for data mining and system identification |
| title_sort | cluster analysis for data mining and system identification |
| topic | Cluster analysis Data mining System identification Systemidentifikation (DE-588)4121753-6 gnd Data Mining (DE-588)4428654-5 gnd Fuzzy-Clusteranalyse (DE-588)4802783-2 gnd |
| topic_facet | Cluster analysis Data mining System identification Systemidentifikation Data Mining Fuzzy-Clusteranalyse |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2848634&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=015451881&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT abonyijanos clusteranalysisfordataminingandsystemidentification AT feilbalazs clusteranalysisfordataminingandsystemidentification |