Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach pro...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on neural networks Vol. 11; no. 5; pp. 1093 - 1105
Main Author Karayiannis, N.B.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.09.2000
Subjects
Agglomeration
Algorithm design and analysis
Algorithms
Clustering
Clustering algorithms
Fuzzy sets
Image segmentation
Learning
Magnetic resonance
Magnetic resonance imaging
Minimization methods
Neural networks
Operators
Partitioning algorithms
Prototypes
Vector quantization
Online AccessGet full text
ISSN1045-9227
DOI10.1109/72.870042

Cover

Abstract This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.
AbstractList This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms
This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.
This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions. Minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft LVQ and clustering algorithms, which includes fuzzy LVQ and clustering algorithms as special cases. The proposed LVQ and clustering algorithms are used to perform segmentation of magnetic resonance (MR) images of the brain. The diagnostic value of the segmented MR images provides the basis for evaluating a variety of ordered weighted LVQ and clustering algorithms.
Author Karayiannis, N.B.
Author_xml – sequence: 1
  givenname: N.B.
  surname: Karayiannis
  fullname: Karayiannis, N.B.
  organization: Dept. of Electr. & Comput. Eng., Houston Univ., TX, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/18249837$$D View this record in MEDLINE/PubMed
BookMark eNp90TlPxDAQBWAXi7gLWgqUiqPYxVdip0SIS0KiAGrLcSbBkI0X2wHBr8eQhQIhKo_k703xZgNNetcDQjsEzwjB5bGgMykw5nSC1gnm-bSkVKyhjRAeMSY8x8UqWiOS8lIysY6ebl0Tsw60723fZi9govPZ86D7aN91tK7PdF9nphtCBP9JdNc6b-PDPGSVDlBniThfg0_jK9j2IaZBt62Hdsy7BXidtoYttNLoLsD28t1E9-dnd6eX0-ubi6vTk-upYSWNU0OYMA1Q4JWpJdFSVLkA3hREgMRFWUktjKhKKA0jjBrJcAUGVwJYXjCu2SY6GPcuvHseIEQ1t8FA1-ke3BCUYIznHOcsyf1_JZW5YCUpEjz8F5JCEJbqlzLRvSUdqjnUauHtXPs39d15AscjMN6F4KFRxsavqqLXtlMEq89LKkHVeMmUOPqV-Fn6h90drQWAH7f8_ACIn6jb
CODEN ITNNEP
CitedBy_id crossref_primary_10_1108_K_06_2013_0107
crossref_primary_10_1049_iet_gtd_2012_0689
crossref_primary_10_1109_TFUZZ_2003_819844
crossref_primary_10_1007_s10726_011_9262_6
crossref_primary_10_1016_j_apm_2013_11_036
crossref_primary_10_1111_itor_12015
crossref_primary_10_1002_int_21585
crossref_primary_10_1016_j_apm_2013_01_022
crossref_primary_10_1109_RBME_2010_2083647
crossref_primary_10_1142_S0219622011004300
crossref_primary_10_1016_j_jcss_2014_03_004
crossref_primary_10_1109_LSP_2004_824054
crossref_primary_10_1080_01969722_2016_1182362
crossref_primary_10_1007_s10726_012_9289_3
crossref_primary_10_3846_20294913_2013_821686
crossref_primary_10_1080_18756891_2011_9727769
crossref_primary_10_1080_01969722_2010_486223
crossref_primary_10_1142_S0218488513500268
crossref_primary_10_3724_SP_J_1001_2009_03410
crossref_primary_10_1109_TIP_2003_817251
crossref_primary_10_1007_s00500_020_05507_1
crossref_primary_10_1016_j_fss_2005_04_003
crossref_primary_10_1016_j_knosys_2012_11_014
crossref_primary_10_3390_e18060171
crossref_primary_10_1109_TNN_2002_806951
crossref_primary_10_1155_2013_563650
crossref_primary_10_1016_j_cie_2010_09_017
crossref_primary_10_1016_j_eswa_2011_02_023
crossref_primary_10_1080_01969722_2015_1012891
crossref_primary_10_3390_su11102820
crossref_primary_10_1016_S0165_0114_03_00184_2
crossref_primary_10_1016_j_eswa_2011_02_104
crossref_primary_10_1108_K_03_2013_0059
crossref_primary_10_1007_s10726_010_9225_3
crossref_primary_10_1016_j_eswa_2010_12_103
crossref_primary_10_1007_s10640_008_9219_7
crossref_primary_10_1016_j_ins_2013_02_039
crossref_primary_10_1007_s10846_006_9093_x
crossref_primary_10_1016_j_cmpb_2014_04_012
crossref_primary_10_1109_MCI_2018_2881641
crossref_primary_10_1109_TNN_2004_841778
crossref_primary_10_1142_S0219622013500296
crossref_primary_10_3233_JIFS_141219
crossref_primary_10_3846_20294913_2015_1056275
crossref_primary_10_1007_s00779_013_0735_2
crossref_primary_10_1155_2017_9634725
crossref_primary_10_1002_int_20444
crossref_primary_10_4018_ijoris_2014070102
crossref_primary_10_1016_j_asoc_2016_11_024
crossref_primary_10_1080_01969722_2015_1012889
crossref_primary_10_1155_2013_705159
crossref_primary_10_1007_s00500_019_03977_6
Cites_doi 10.1109/42.56342
10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.3.CO;2-L
10.1002/mrm.1910110103
10.1016/0165-0114(93)90194-M
10.1109/72.788654
10.1109/91.388178
10.1109/83.503919
10.1007/978-1-4757-0450-1
10.1109/21.87068
10.1109/83.480771
10.1109/72.536304
10.1109/83.413164
10.1109/72.536314
10.1109/42.57771
10.1109/3477.485833
10.1109/91.649915
10.1016/0165-0114(84)90097-6
10.1109/FUZZY.1998.686331
10.1109/72.159057
10.1016/0031-3203(94)90052-3
10.1117/12.269766
10.1016/0020-0255(85)90027-1
10.1109/91.669028
10.1109/72.572091
10.1016/0893-6080(95)00024-T
10.3233/IFS-1997-5202
10.1109/FUZZY.1998.686322
10.1109/78.124940
10.1016/S0362-546X(97)00378-7
ContentType Journal Article
DBID RIA
RIE
AAYXX
CITATION
NPM
7SC
7SP
8FD
F28
FR3
JQ2
L7M
L~C
L~D
7X8
DOI 10.1109/72.870042
DatabaseName IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
PubMed
Computer and Information Systems Abstracts
Electronics & Communications Abstracts
Technology Research Database
ANTE: Abstracts in New Technology & Engineering
Engineering Research Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
MEDLINE - Academic
DatabaseTitle CrossRef
PubMed
Technology Research Database
Computer and Information Systems Abstracts – Academic
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
ANTE: Abstracts in New Technology & Engineering
Computer and Information Systems Abstracts Professional
MEDLINE - Academic
DatabaseTitleList Computer and Information Systems Abstracts
MEDLINE - Academic

Technology Research Database
PubMed
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
Anatomy & Physiology
Computer Science
EndPage 1105
ExternalDocumentID 18249837
10_1109_72_870042
870042
Genre Journal Article
GroupedDBID ---
-~X
.DC
0R~
29I
4.4
53G
5GY
5VS
6IK
97E
AAJGR
AASAJ
AAWTH
ABAZT
ABJNI
ABQJQ
ABVLG
ACGFS
AETIX
AGQYO
AGSQL
AHBIQ
AI.
AIBXA
ALLEH
ALMA_UNASSIGNED_HOLDINGS
ASUFR
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
F5P
HZ~
H~9
ICLAB
IFIPE
IFJZH
IPLJI
JAVBF
LAI
M43
MS~
O9-
OCL
P2P
RIA
RIE
RNS
S10
TAE
TN5
VH1
AAYXX
CITATION
AAYOK
NPM
RIG
7SC
7SP
8FD
F28
FR3
JQ2
L7M
L~C
L~D
7X8
ID FETCH-LOGICAL-c392t-c137cfe2e4bcd81a87b57e4f617e8069b8a7c7b9e9c3132c830bec0b7e35634a3
IEDL.DBID RIE
ISSN 1045-9227
IngestDate Thu Sep 04 19:02:40 EDT 2025
Thu Sep 04 17:49:27 EDT 2025
Thu Sep 04 18:32:42 EDT 2025
Thu Apr 03 06:58:26 EDT 2025
Thu Apr 24 22:55:08 EDT 2025
Wed Oct 01 03:24:37 EDT 2025
Tue Aug 26 21:00:27 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 5
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c392t-c137cfe2e4bcd81a87b57e4f617e8069b8a7c7b9e9c3132c830bec0b7e35634a3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
PMID 18249837
PQID 1671310988
PQPubID 23500
PageCount 13
ParticipantIDs proquest_miscellaneous_28573916
crossref_citationtrail_10_1109_72_870042
proquest_miscellaneous_1671310988
pubmed_primary_18249837
ieee_primary_870042
proquest_miscellaneous_733454053
crossref_primary_10_1109_72_870042
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2000-09-01
PublicationDateYYYYMMDD 2000-09-01
PublicationDate_xml – month: 09
  year: 2000
  text: 2000-09-01
  day: 01
PublicationDecade 2000
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle IEEE transactions on neural networks
PublicationTitleAbbrev TNN
PublicationTitleAlternate IEEE Trans Neural Netw
PublicationYear 2000
Publisher IEEE
Publisher_xml – name: IEEE
References ref13
ref14
ref31
ref30
ref11
ref32
karayiannis (ref10) 1997; 1
karayiannis (ref9) 1997; 5
ref2
ref1
ref17
ref16
karayiannis (ref15) 2000; 8
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref28
ref27
ref29
ref8
ref7
you (ref33) 1995
ref4
ref3
ref6
ref5
karayiannis (ref12) 1997; 3030
klir (ref21) 1995
References_xml – ident: ref3
  doi: 10.1109/42.56342
– start-page: 2763
  year: 1995
  ident: ref33
  article-title: lvq with a weighted objective function
  publication-title: Proc IEEE Int Conf Neural Networks
– ident: ref29
  doi: 10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.3.CO;2-L
– ident: ref8
  doi: 10.1002/mrm.1910110103
– ident: ref28
  doi: 10.1016/0165-0114(93)90194-M
– ident: ref16
  doi: 10.1109/72.788654
– ident: ref7
  doi: 10.1109/91.388178
– volume: 1
  start-page: 33
  year: 1997
  ident: ref10
  article-title: learning vector quantization: a review
  publication-title: Int J Smart Eng Syst Design
– ident: ref23
  doi: 10.1109/83.503919
– ident: ref1
  doi: 10.1007/978-1-4757-0450-1
– ident: ref27
  doi: 10.1109/21.87068
– ident: ref22
  doi: 10.1109/83.480771
– ident: ref17
  doi: 10.1109/72.536304
– year: 1995
  ident: ref21
  publication-title: Fuzzy Sets and Fuzzy Logic Theory and Applications
– ident: ref19
  doi: 10.1109/83.413164
– ident: ref20
  doi: 10.1109/72.536314
– ident: ref25
  doi: 10.1109/42.57771
– ident: ref30
  doi: 10.1109/3477.485833
– ident: ref18
  doi: 10.1109/91.649915
– ident: ref5
  doi: 10.1016/0165-0114(84)90097-6
– ident: ref14
  doi: 10.1109/FUZZY.1998.686331
– ident: ref6
  doi: 10.1109/72.159057
– ident: ref26
  doi: 10.1016/0031-3203(94)90052-3
– volume: 3030
  start-page: 2
  year: 1997
  ident: ref12
  article-title: entropy constrained learning vector quantization algorithms and their application in image compression
  publication-title: Proc SPIE
  doi: 10.1117/12.269766
– ident: ref4
  doi: 10.1016/0020-0255(85)90027-1
– ident: ref31
  doi: 10.1109/91.669028
– ident: ref11
  doi: 10.1109/72.572091
– ident: ref2
  doi: 10.1016/0893-6080(95)00024-T
– volume: 5
  start-page: 103
  year: 1997
  ident: ref9
  article-title: fuzzy partition entropies and entropy constrained fuzzy clustering algorithms
  publication-title: J Intell Fuzzy Syst
  doi: 10.3233/IFS-1997-5202
– volume: 8
  start-page: 63
  year: 2000
  ident: ref15
  article-title: generalized fuzzy $c$-means algorithms
  publication-title: J Intell Fuzzy Syst
– ident: ref13
  doi: 10.1109/FUZZY.1998.686322
– ident: ref32
  doi: 10.1109/78.124940
– ident: ref24
  doi: 10.1016/S0362-546X(97)00378-7
SSID ssj0014506
Score 1.9912087
Snippet This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These...
SourceID proquest
pubmed
crossref
ieee
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 1093
SubjectTerms Agglomeration
Algorithm design and analysis
Algorithms
Clustering
Clustering algorithms
Fuzzy sets
Image segmentation
Learning
Magnetic resonance
Magnetic resonance imaging
Minimization methods
Neural networks
Operators
Partitioning algorithms
Prototypes
Vector quantization
Title Soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators
URI https://ieeexplore.ieee.org/document/870042
https://www.ncbi.nlm.nih.gov/pubmed/18249837
https://www.proquest.com/docview/1671310988
https://www.proquest.com/docview/28573916
https://www.proquest.com/docview/733454053
Volume 11
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  issn: 1045-9227
  databaseCode: RIE
  dateStart: 19900101
  customDbUrl:
  isFulltext: true
  dateEnd: 20111231
  titleUrlDefault: https://ieeexplore.ieee.org/
  omitProxy: false
  ssIdentifier: ssj0014506
  providerName: IEEE
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB5BT3CgsAWa8jIIIS7ZJnYcO8cKUVUcuECl3iLbmSyo26R0Eyr49Yzt7PJQV-JmRePEisf2fJ6ZbwBeG2FyaY1MKydUWrSNSo2oTMqLVgs0eWtMYPv8WJ6cFh_O5NnEsx1yYRAxBJ_h3DeDL7_p3eivyg6152Kn_fa20mVM1do4DAoZymgSuKDPcq4mEqE8qw4Vn8eOfx09oZbKdrMyHC_HuzFvexVYCX1Uyfl8HOzc_fyHs_E_R34f7k1mJjuKevEAbmE3g72jjiD2xQ_2hoXAz3CjPoPddWUHNi30Gdz9g6ZwD84_0V7NpvoSC_Y9XPSzbyNNypTFyUzXMLccPeuCFzHLRX_1dfhysWL-mGwYiQSST2peh8tYapgFYf1F7N9fYvD3rx7C6fH7z-9O0qlIQ-rItBpSlwvlWuRYWNfo3GhlpcKiJcsIdVZWVhvllK2QlIGQr9MiI7XJrEIhS1EY8Qh2ur7DfWA2RyQA1ebYtgU6T4UvK56h5E7lUqsE3q7nr3YTg7kvpLGsA5LJqlrxOv7pBF5tRC8jbcdNQjM_UxuB9dOXa52oaal5_4npsB9XdV4Soqc3aJ3Aiy0yXEvlc5kTYFsklBCe9VCKBB5Hjfs9Qk1gWAt1cOPAnsCdyAPgA9yews5wNeIzsogG-zyshV8ymguz
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6hcgAOtGx5BFpqEEJcsk38WDvHqqJaoPRCK_UWOc5kQd0mpZuA4NczdrLLQ12JmxWNEyse2_N5Zr4BeGWFTVVhVZw5oWNZlTq2IrMxl5URaNPK2sD2eTKZnsn35-p84NkOuTCIGILPcOybwZdfNq7zV2X7xnOx0357W0kpVZ-stXIZSBUKaRK8oA9zrgcaoTTJ9jUf913_OnxCNZX1hmU4YI42-8ztReAl9HElF-OuLcbu5z-sjf859i24Pxia7KDXjAdwC-sRbB_UBLIvf7DXLIR-hjv1EWwuazuwYamP4N4fRIXbcPGJdms2VJiYsW_hqp997WhahjxOZuuSuXnneRe8iJ3Pmusv7efLBfMHZclIJNB8UvN7uI6lhp0R2p_1_ZsrDB7_xUM4O3p7ejiNhzINsSPjqo1dKrSrkKMsXGlSa3ShNMqKbCM0ySQrjNVOFxmSOhD2dUYkpDhJoVGoiZBWPIKNuqnxCbAiRSQIVaVYVRKdJ8NXGU9QcadTZXQEb5bzl7uBw9yX0pjnAcskWa553v_pCF6uRK964o6bhEZ-plYCy6cvljqR02LzHhRbY9Mt8nRCmJ7eYEwEe2tkuFHaZzNHwNZIaCE876ESETzuNe73CA3BYSP00xsHtgd3pqcfj_PjdycfnsHdnhXAh7vtwEZ73eEu2Udt8Tysi19PEQ8A
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Soft+learning+vector+quantization+and+clustering+algorithms+based+on+ordered+weighted+aggregation+operators&rft.jtitle=IEEE+transactions+on+neural+networks&rft.au=Karayiannis%2C+N.B.&rft.date=2000-09-01&rft.issn=1045-9227&rft.volume=11&rft.issue=5&rft.spage=1093&rft.epage=1105&rft_id=info:doi/10.1109%2F72.870042&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_72_870042
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1045-9227&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1045-9227&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1045-9227&client=summon