A global optimization method for semi-supervised clustering
Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algor...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xia, Yu [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2008 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC 2008 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Data mining and knowledge discovery - Springer US, 1997, 18(2008), 2 vom: 18. Juni, Seite 214-256 |
|---|---|
| Übergeordnetes Werk: |
volume:18 ; year:2008 ; number:2 ; day:18 ; month:06 ; pages:214-256 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10618-008-0104-3 |
|---|
| Katalog-ID: |
OLC202705829X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC202705829X | ||
| 003 | DE-627 | ||
| 005 | 20230503034702.0 | ||
| 007 | tu | ||
| 008 | 200819s2008 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10618-008-0104-3 |2 doi | |
| 035 | |a (DE-627)OLC202705829X | ||
| 035 | |a (DE-He213)s10618-008-0104-3-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 400 |a 070 |a 004 |q VZ |
| 084 | |a 24,1 |2 ssgn | ||
| 084 | |a LING |q DE-30 |2 fid | ||
| 100 | 1 | |a Xia, Yu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A global optimization method for semi-supervised clustering |
| 264 | 1 | |c 2008 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC 2008 | ||
| 520 | |a Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. | ||
| 650 | 4 | |a Partitional clustering | |
| 650 | 4 | |a Semi-supervised clustering | |
| 650 | 4 | |a Instance-level constraints | |
| 650 | 4 | |a Global optimization | |
| 650 | 4 | |a Concavity cuts | |
| 773 | 0 | 8 | |i Enthalten in |t Data mining and knowledge discovery |d Springer US, 1997 |g 18(2008), 2 vom: 18. Juni, Seite 214-256 |w (DE-627)230491774 |w (DE-600)1386325-3 |w (DE-576)067290434 |x 1384-5810 |7 nnns |
| 773 | 1 | 8 | |g volume:18 |g year:2008 |g number:2 |g day:18 |g month:06 |g pages:214-256 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10618-008-0104-3 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a FID-LING | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OPC-BBI | ||
| 912 | |a SSG-OPC-ANG | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_4012 | ||
| 912 | |a GBV_ILN_4305 | ||
| 951 | |a AR | ||
| 952 | |d 18 |j 2008 |e 2 |b 18 |c 06 |h 214-256 | ||
| author_variant |
y x yx |
|---|---|
| matchkey_str |
article:13845810:2008----::goaotmztomtofreiuev |
| hierarchy_sort_str |
2008 |
| publishDate |
2008 |
| allfields |
10.1007/s10618-008-0104-3 doi (DE-627)OLC202705829X (DE-He213)s10618-008-0104-3-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Xia, Yu verfasserin aut A global optimization method for semi-supervised clustering 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. Partitional clustering Semi-supervised clustering Instance-level constraints Global optimization Concavity cuts Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2008), 2 vom: 18. Juni, Seite 214-256 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2008 number:2 day:18 month:06 pages:214-256 https://doi.org/10.1007/s10618-008-0104-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4012 GBV_ILN_4305 AR 18 2008 2 18 06 214-256 |
| spelling |
10.1007/s10618-008-0104-3 doi (DE-627)OLC202705829X (DE-He213)s10618-008-0104-3-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Xia, Yu verfasserin aut A global optimization method for semi-supervised clustering 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. Partitional clustering Semi-supervised clustering Instance-level constraints Global optimization Concavity cuts Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2008), 2 vom: 18. Juni, Seite 214-256 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2008 number:2 day:18 month:06 pages:214-256 https://doi.org/10.1007/s10618-008-0104-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4012 GBV_ILN_4305 AR 18 2008 2 18 06 214-256 |
| allfields_unstemmed |
10.1007/s10618-008-0104-3 doi (DE-627)OLC202705829X (DE-He213)s10618-008-0104-3-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Xia, Yu verfasserin aut A global optimization method for semi-supervised clustering 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. Partitional clustering Semi-supervised clustering Instance-level constraints Global optimization Concavity cuts Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2008), 2 vom: 18. Juni, Seite 214-256 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2008 number:2 day:18 month:06 pages:214-256 https://doi.org/10.1007/s10618-008-0104-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4012 GBV_ILN_4305 AR 18 2008 2 18 06 214-256 |
| allfieldsGer |
10.1007/s10618-008-0104-3 doi (DE-627)OLC202705829X (DE-He213)s10618-008-0104-3-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Xia, Yu verfasserin aut A global optimization method for semi-supervised clustering 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. Partitional clustering Semi-supervised clustering Instance-level constraints Global optimization Concavity cuts Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2008), 2 vom: 18. Juni, Seite 214-256 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2008 number:2 day:18 month:06 pages:214-256 https://doi.org/10.1007/s10618-008-0104-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4012 GBV_ILN_4305 AR 18 2008 2 18 06 214-256 |
| allfieldsSound |
10.1007/s10618-008-0104-3 doi (DE-627)OLC202705829X (DE-He213)s10618-008-0104-3-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Xia, Yu verfasserin aut A global optimization method for semi-supervised clustering 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2008 Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. Partitional clustering Semi-supervised clustering Instance-level constraints Global optimization Concavity cuts Enthalten in Data mining and knowledge discovery Springer US, 1997 18(2008), 2 vom: 18. Juni, Seite 214-256 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:18 year:2008 number:2 day:18 month:06 pages:214-256 https://doi.org/10.1007/s10618-008-0104-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4012 GBV_ILN_4305 AR 18 2008 2 18 06 214-256 |
| language |
English |
| source |
Enthalten in Data mining and knowledge discovery 18(2008), 2 vom: 18. Juni, Seite 214-256 volume:18 year:2008 number:2 day:18 month:06 pages:214-256 |
| sourceStr |
Enthalten in Data mining and knowledge discovery 18(2008), 2 vom: 18. Juni, Seite 214-256 volume:18 year:2008 number:2 day:18 month:06 pages:214-256 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Partitional clustering Semi-supervised clustering Instance-level constraints Global optimization Concavity cuts |
| dewey-raw |
400 |
| isfreeaccess_bool |
false |
| container_title |
Data mining and knowledge discovery |
| authorswithroles_txt_mv |
Xia, Yu @@aut@@ |
| publishDateDaySort_date |
2008-06-18T00:00:00Z |
| hierarchy_top_id |
230491774 |
| dewey-sort |
3400 |
| id |
OLC202705829X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC202705829X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503034702.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2008 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10618-008-0104-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC202705829X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10618-008-0104-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">400</subfield><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xia, Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A global optimization method for semi-supervised clustering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2008</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partitional clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semi-supervised clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Instance-level constraints</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Global optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Concavity cuts</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Data mining and knowledge discovery</subfield><subfield code="d">Springer US, 1997</subfield><subfield code="g">18(2008), 2 vom: 18. Juni, Seite 214-256</subfield><subfield code="w">(DE-627)230491774</subfield><subfield code="w">(DE-600)1386325-3</subfield><subfield code="w">(DE-576)067290434</subfield><subfield code="x">1384-5810</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:18</subfield><subfield code="g">year:2008</subfield><subfield code="g">number:2</subfield><subfield code="g">day:18</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:214-256</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10618-008-0104-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-LING</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ANG</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">18</subfield><subfield code="j">2008</subfield><subfield code="e">2</subfield><subfield code="b">18</subfield><subfield code="c">06</subfield><subfield code="h">214-256</subfield></datafield></record></collection>
|
| author |
Xia, Yu |
| spellingShingle |
Xia, Yu ddc 400 ssgn 24,1 fid LING misc Partitional clustering misc Semi-supervised clustering misc Instance-level constraints misc Global optimization misc Concavity cuts A global optimization method for semi-supervised clustering |
| authorStr |
Xia, Yu |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)230491774 |
| format |
Article |
| dewey-ones |
400 - Language 070 - News media, journalism & publishing 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1384-5810 |
| topic_title |
400 070 004 VZ 24,1 ssgn LING DE-30 fid A global optimization method for semi-supervised clustering Partitional clustering Semi-supervised clustering Instance-level constraints Global optimization Concavity cuts |
| topic |
ddc 400 ssgn 24,1 fid LING misc Partitional clustering misc Semi-supervised clustering misc Instance-level constraints misc Global optimization misc Concavity cuts |
| topic_unstemmed |
ddc 400 ssgn 24,1 fid LING misc Partitional clustering misc Semi-supervised clustering misc Instance-level constraints misc Global optimization misc Concavity cuts |
| topic_browse |
ddc 400 ssgn 24,1 fid LING misc Partitional clustering misc Semi-supervised clustering misc Instance-level constraints misc Global optimization misc Concavity cuts |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Data mining and knowledge discovery |
| hierarchy_parent_id |
230491774 |
| dewey-tens |
400 - Language 070 - News media, journalism & publishing 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Data mining and knowledge discovery |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 |
| title |
A global optimization method for semi-supervised clustering |
| ctrlnum |
(DE-627)OLC202705829X (DE-He213)s10618-008-0104-3-p |
| title_full |
A global optimization method for semi-supervised clustering |
| author_sort |
Xia, Yu |
| journal |
Data mining and knowledge discovery |
| journalStr |
Data mining and knowledge discovery |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
400 - Language 000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2008 |
| contenttype_str_mv |
txt |
| container_start_page |
214 |
| author_browse |
Xia, Yu |
| container_volume |
18 |
| class |
400 070 004 VZ 24,1 ssgn LING DE-30 fid |
| format_se |
Aufsätze |
| author-letter |
Xia, Yu |
| doi_str_mv |
10.1007/s10618-008-0104-3 |
| dewey-full |
400 070 004 |
| title_sort |
a global optimization method for semi-supervised clustering |
| title_auth |
A global optimization method for semi-supervised clustering |
| abstract |
Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. © Springer Science+Business Media, LLC 2008 |
| abstractGer |
Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. © Springer Science+Business Media, LLC 2008 |
| abstract_unstemmed |
Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do. © Springer Science+Business Media, LLC 2008 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4012 GBV_ILN_4305 |
| container_issue |
2 |
| title_short |
A global optimization method for semi-supervised clustering |
| url |
https://doi.org/10.1007/s10618-008-0104-3 |
| remote_bool |
false |
| ppnlink |
230491774 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10618-008-0104-3 |
| up_date |
2024-07-03T13:36:48.270Z |
| _version_ |
1803565199019474944 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC202705829X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503034702.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2008 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10618-008-0104-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC202705829X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10618-008-0104-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">400</subfield><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xia, Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A global optimization method for semi-supervised clustering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2008</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we adapt Tuy’s concave cutting plane method to the semi-supervised clustering. We also give properties of local optimal solutions of the semi-supervised clustering. Numerical examples show that this method can give a better solution than other semi-supervised clustering algorithms do.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partitional clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semi-supervised clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Instance-level constraints</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Global optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Concavity cuts</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Data mining and knowledge discovery</subfield><subfield code="d">Springer US, 1997</subfield><subfield code="g">18(2008), 2 vom: 18. Juni, Seite 214-256</subfield><subfield code="w">(DE-627)230491774</subfield><subfield code="w">(DE-600)1386325-3</subfield><subfield code="w">(DE-576)067290434</subfield><subfield code="x">1384-5810</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:18</subfield><subfield code="g">year:2008</subfield><subfield code="g">number:2</subfield><subfield code="g">day:18</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:214-256</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10618-008-0104-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-LING</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ANG</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">18</subfield><subfield code="j">2008</subfield><subfield code="e">2</subfield><subfield code="b">18</subfield><subfield code="c">06</subfield><subfield code="h">214-256</subfield></datafield></record></collection>
|
| score |
7.400769 |