K-means Multiple Clustering Research Based on Pseudo Parallel Genetic Algorithm
As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, ad...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 33 |
|---|---|
| container_issue | |
| container_start_page | 30 |
| container_title | |
| container_volume | 1 |
| creator | Ge Xiufeng Xing Changzheng |
| description | As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, adopt the strategy of Variable-Length Chromosome real-coded. Through the introduction of chromosome retreading and focusing operator, K-means algorithm can be perfectly combined with pseudo-parallel genetic algorithm. For the dynamic and directed adjustment of migration rate with the evolutionary process, we have improved the migration rate of PPGA. The results of repeated experiment show that the method can effectively solve the previous problem and it is a practical and effective clustering algorithm. |
| doi_str_mv | 10.1109/IFITA.2010.186 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5635035</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5635035</ieee_id><sourcerecordid>5635035</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-38e71653a242574504e87110a336266812b355c71faeb5e289d51dc57999be193</originalsourceid><addsrcrecordid>eNo1j8tKw0AYRkekoK3ZunEzL5A698syBluDlRbJvkySv-3IJCmZZOHb26KuDgcOH3wIPVKypJTY52JVlNmSkasbdYMSqw0VTAitGCO3aP4vVM3Q_NpZZrTUdyiJ8YsQQq3Sioh7tH1PW3BdxB9TGP05AM7DFEcYfHfEnxDBDfUJv7gIDe47vIswNT3eucGFAAGvoYPR1zgLx37w46l9QLODCxGSPy5QuXot87d0s10XebZJvSVjyg1oqiR3TDCphSQCjL48c5wrppShrOJS1poeHFQSmLGNpE0ttbW2Amr5Aj39znoA2J8H37rhey8Vl4RL_gOxRU8x</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>K-means Multiple Clustering Research Based on Pseudo Parallel Genetic Algorithm</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Ge Xiufeng ; Xing Changzheng</creator><creatorcontrib>Ge Xiufeng ; Xing Changzheng</creatorcontrib><description>As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, adopt the strategy of Variable-Length Chromosome real-coded. Through the introduction of chromosome retreading and focusing operator, K-means algorithm can be perfectly combined with pseudo-parallel genetic algorithm. For the dynamic and directed adjustment of migration rate with the evolutionary process, we have improved the migration rate of PPGA. The results of repeated experiment show that the method can effectively solve the previous problem and it is a practical and effective clustering algorithm.</description><identifier>ISBN: 1424476216</identifier><identifier>ISBN: 9781424476213</identifier><identifier>EISBN: 9781424476220</identifier><identifier>EISBN: 1424476224</identifier><identifier>DOI: 10.1109/IFITA.2010.186</identifier><identifier>LCCN: 2010928757</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Algorithm design and analysis ; Biological cells ; Chromosome retreading ; Clustering ; Clustering algorithms ; Clustering methods ; Convergence ; Focusing operator ; Migration strategy ; Partitioning algorithms ; Pseudo-parallel genetic Algorithm</subject><ispartof>2010 International Forum on Information Technology and Applications, 2010, Vol.1, p.30-33</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5635035$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5635035$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ge Xiufeng</creatorcontrib><creatorcontrib>Xing Changzheng</creatorcontrib><title>K-means Multiple Clustering Research Based on Pseudo Parallel Genetic Algorithm</title><title>2010 International Forum on Information Technology and Applications</title><addtitle>ifita</addtitle><description>As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, adopt the strategy of Variable-Length Chromosome real-coded. Through the introduction of chromosome retreading and focusing operator, K-means algorithm can be perfectly combined with pseudo-parallel genetic algorithm. For the dynamic and directed adjustment of migration rate with the evolutionary process, we have improved the migration rate of PPGA. The results of repeated experiment show that the method can effectively solve the previous problem and it is a practical and effective clustering algorithm.</description><subject>Accuracy</subject><subject>Algorithm design and analysis</subject><subject>Biological cells</subject><subject>Chromosome retreading</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>Convergence</subject><subject>Focusing operator</subject><subject>Migration strategy</subject><subject>Partitioning algorithms</subject><subject>Pseudo-parallel genetic Algorithm</subject><isbn>1424476216</isbn><isbn>9781424476213</isbn><isbn>9781424476220</isbn><isbn>1424476224</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j8tKw0AYRkekoK3ZunEzL5A698syBluDlRbJvkySv-3IJCmZZOHb26KuDgcOH3wIPVKypJTY52JVlNmSkasbdYMSqw0VTAitGCO3aP4vVM3Q_NpZZrTUdyiJ8YsQQq3Sioh7tH1PW3BdxB9TGP05AM7DFEcYfHfEnxDBDfUJv7gIDe47vIswNT3eucGFAAGvoYPR1zgLx37w46l9QLODCxGSPy5QuXot87d0s10XebZJvSVjyg1oqiR3TDCphSQCjL48c5wrppShrOJS1poeHFQSmLGNpE0ttbW2Amr5Aj39znoA2J8H37rhey8Vl4RL_gOxRU8x</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Ge Xiufeng</creator><creator>Xing Changzheng</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>K-means Multiple Clustering Research Based on Pseudo Parallel Genetic Algorithm</title><author>Ge Xiufeng ; Xing Changzheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-38e71653a242574504e87110a336266812b355c71faeb5e289d51dc57999be193</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Accuracy</topic><topic>Algorithm design and analysis</topic><topic>Biological cells</topic><topic>Chromosome retreading</topic><topic>Clustering</topic><topic>Clustering algorithms</topic><topic>Clustering methods</topic><topic>Convergence</topic><topic>Focusing operator</topic><topic>Migration strategy</topic><topic>Partitioning algorithms</topic><topic>Pseudo-parallel genetic Algorithm</topic><toplevel>online_resources</toplevel><creatorcontrib>Ge Xiufeng</creatorcontrib><creatorcontrib>Xing Changzheng</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ge Xiufeng</au><au>Xing Changzheng</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>K-means Multiple Clustering Research Based on Pseudo Parallel Genetic Algorithm</atitle><btitle>2010 International Forum on Information Technology and Applications</btitle><stitle>ifita</stitle><date>2010-07</date><risdate>2010</risdate><volume>1</volume><spage>30</spage><epage>33</epage><pages>30-33</pages><isbn>1424476216</isbn><isbn>9781424476213</isbn><eisbn>9781424476220</eisbn><eisbn>1424476224</eisbn><abstract>As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, adopt the strategy of Variable-Length Chromosome real-coded. Through the introduction of chromosome retreading and focusing operator, K-means algorithm can be perfectly combined with pseudo-parallel genetic algorithm. For the dynamic and directed adjustment of migration rate with the evolutionary process, we have improved the migration rate of PPGA. The results of repeated experiment show that the method can effectively solve the previous problem and it is a practical and effective clustering algorithm.</abstract><pub>IEEE</pub><doi>10.1109/IFITA.2010.186</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 1424476216 |
| ispartof | 2010 International Forum on Information Technology and Applications, 2010, Vol.1, p.30-33 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5635035 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Algorithm design and analysis Biological cells Chromosome retreading Clustering Clustering algorithms Clustering methods Convergence Focusing operator Migration strategy Partitioning algorithms Pseudo-parallel genetic Algorithm |
| title | K-means Multiple Clustering Research Based on Pseudo Parallel Genetic Algorithm |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T07%3A25%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=K-means%20Multiple%20Clustering%20Research%20Based%20on%20Pseudo%20Parallel%20Genetic%20Algorithm&rft.btitle=2010%20International%20Forum%20on%20Information%20Technology%20and%20Applications&rft.au=Ge%20Xiufeng&rft.date=2010-07&rft.volume=1&rft.spage=30&rft.epage=33&rft.pages=30-33&rft.isbn=1424476216&rft.isbn_list=9781424476213&rft_id=info:doi/10.1109/IFITA.2010.186&rft_dat=%3Cieee_6IE%3E5635035%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424476220&rft.eisbn_list=1424476224&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5635035&rfr_iscdi=true |