CA-Tree: A Hierarchical Structure for Efficient and Scalable Coassociation-Based Cluster Ensembles
Cluster ensembles have attracted a lot of research interests in recent years, and their applications continue to expand. Among the various algorithms for cluster ensembles, those based on coassociation matrices are probably the ones studied and used the most because coassociation matrices are easy t...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on cybernetics 2011-06, Vol.41 (3), p.686-698 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 698 |
|---|---|
| container_issue | 3 |
| container_start_page | 686 |
| container_title | IEEE transactions on cybernetics |
| container_volume | 41 |
| creator | Wang, Tsaipei |
| description | Cluster ensembles have attracted a lot of research interests in recent years, and their applications continue to expand. Among the various algorithms for cluster ensembles, those based on coassociation matrices are probably the ones studied and used the most because coassociation matrices are easy to understand and implement. However, the main limitation of coassociation matrices as the data structure for combining multiple clusterings is the complexity that is at least quadratic to the number of patterns N . In this paper, we propose CA-tree, which is a dendogram-like hierarchical data structure, to facilitate efficient and scalable cluster ensembles for coassociation-matrix-based algorithms. All the properties of the CA-tree are derived from base cluster labels and do not require the access to the original data features. We then apply a threshold to the CA-tree to obtain a set of nodes, which are then used in place of the original patterns for ensemble-clustering algorithms. The experiments demonstrate that the complexity for coassociation-based cluster ensembles can be reduced to close to linear to N with minimal loss on clustering accuracy. |
| doi_str_mv | 10.1109/TSMCB.2010.2086059 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_889436119</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5625918</ieee_id><sourcerecordid>889436119</sourcerecordid><originalsourceid>FETCH-LOGICAL-c447t-37c6464b1d217786a43501c21eab87e5f55046e408b04be82dc9c3a87a0bc9153</originalsourceid><addsrcrecordid>eNqFkUtP3DAURi3UisfQPwASsrrpKvTa8bO7IeJRiYrFTNeR49wIo0wCdrLg39fTGViwYXV95fN90tUh5IzBJWNgf65Xf6qrSw5552AUSHtAjpkVrABh-Zf8BlMWQjB7RE5SegIAC1YfkiPOQEvN9TFpqmWxjoi_6JLeBYwu-sfgXU9XU5z9NEek3RjpddcFH3CYqBtausqAa3qk1ehSGn1wUxiH4solbGnVz2nCHBkSbjKUTsnXzvUJv-3ngvy9uV5Xd8X9w-3vanlfeCH0VJTaK6FEw1rOtDbKiVIC85yha4xG2UkJQqEA04Bo0PDWW186ox003jJZLsiPXe9zHF9mTFO9Cclj37sBxznVxlhRKsbs56QywLUpVSa_fyCfxjkO-YwMabHltnV8B_k4phSxq59j2Lj4WjOot6bq_6bqral6byqHLvbNc7PB9j3ypiYD5zsgIOL7t1RcWmbKf9uLlZQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>867468029</pqid></control><display><type>article</type><title>CA-Tree: A Hierarchical Structure for Efficient and Scalable Coassociation-Based Cluster Ensembles</title><source>IEEE Electronic Library (IEL)</source><creator>Wang, Tsaipei</creator><creatorcontrib>Wang, Tsaipei</creatorcontrib><description>Cluster ensembles have attracted a lot of research interests in recent years, and their applications continue to expand. Among the various algorithms for cluster ensembles, those based on coassociation matrices are probably the ones studied and used the most because coassociation matrices are easy to understand and implement. However, the main limitation of coassociation matrices as the data structure for combining multiple clusterings is the complexity that is at least quadratic to the number of patterns N . In this paper, we propose CA-tree, which is a dendogram-like hierarchical data structure, to facilitate efficient and scalable cluster ensembles for coassociation-matrix-based algorithms. All the properties of the CA-tree are derived from base cluster labels and do not require the access to the original data features. We then apply a threshold to the CA-tree to obtain a set of nodes, which are then used in place of the original patterns for ensemble-clustering algorithms. The experiments demonstrate that the complexity for coassociation-based cluster ensembles can be reduced to close to linear to N with minimal loss on clustering accuracy.</description><identifier>ISSN: 1083-4419</identifier><identifier>ISSN: 2168-2267</identifier><identifier>EISSN: 1941-0492</identifier><identifier>EISSN: 2168-2275</identifier><identifier>DOI: 10.1109/TSMCB.2010.2086059</identifier><identifier>PMID: 21075727</identifier><identifier>CODEN: ITSCFI</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Artificial Intelligence ; Buildings ; Cluster Analysis ; Cluster ensemble ; Clustering algorithms ; Clusters ; coassociation matrix ; Complexity ; Complexity theory ; Computer Simulation ; Cybernetics ; Data structures ; Decision Support Techniques ; Diversity reception ; Mathematical analysis ; Matrices ; Matrix methods ; Models, Theoretical ; multiple clusterings ; Partitioning algorithms ; Pattern Recognition, Automated - methods ; Prototypes ; Upper bound</subject><ispartof>IEEE transactions on cybernetics, 2011-06, Vol.41 (3), p.686-698</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jun 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-37c6464b1d217786a43501c21eab87e5f55046e408b04be82dc9c3a87a0bc9153</citedby><cites>FETCH-LOGICAL-c447t-37c6464b1d217786a43501c21eab87e5f55046e408b04be82dc9c3a87a0bc9153</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5625918$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5625918$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21075727$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Tsaipei</creatorcontrib><title>CA-Tree: A Hierarchical Structure for Efficient and Scalable Coassociation-Based Cluster Ensembles</title><title>IEEE transactions on cybernetics</title><addtitle>TSMCB</addtitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><description>Cluster ensembles have attracted a lot of research interests in recent years, and their applications continue to expand. Among the various algorithms for cluster ensembles, those based on coassociation matrices are probably the ones studied and used the most because coassociation matrices are easy to understand and implement. However, the main limitation of coassociation matrices as the data structure for combining multiple clusterings is the complexity that is at least quadratic to the number of patterns N . In this paper, we propose CA-tree, which is a dendogram-like hierarchical data structure, to facilitate efficient and scalable cluster ensembles for coassociation-matrix-based algorithms. All the properties of the CA-tree are derived from base cluster labels and do not require the access to the original data features. We then apply a threshold to the CA-tree to obtain a set of nodes, which are then used in place of the original patterns for ensemble-clustering algorithms. The experiments demonstrate that the complexity for coassociation-based cluster ensembles can be reduced to close to linear to N with minimal loss on clustering accuracy.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Buildings</subject><subject>Cluster Analysis</subject><subject>Cluster ensemble</subject><subject>Clustering algorithms</subject><subject>Clusters</subject><subject>coassociation matrix</subject><subject>Complexity</subject><subject>Complexity theory</subject><subject>Computer Simulation</subject><subject>Cybernetics</subject><subject>Data structures</subject><subject>Decision Support Techniques</subject><subject>Diversity reception</subject><subject>Mathematical analysis</subject><subject>Matrices</subject><subject>Matrix methods</subject><subject>Models, Theoretical</subject><subject>multiple clusterings</subject><subject>Partitioning algorithms</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Prototypes</subject><subject>Upper bound</subject><issn>1083-4419</issn><issn>2168-2267</issn><issn>1941-0492</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkUtP3DAURi3UisfQPwASsrrpKvTa8bO7IeJRiYrFTNeR49wIo0wCdrLg39fTGViwYXV95fN90tUh5IzBJWNgf65Xf6qrSw5552AUSHtAjpkVrABh-Zf8BlMWQjB7RE5SegIAC1YfkiPOQEvN9TFpqmWxjoi_6JLeBYwu-sfgXU9XU5z9NEek3RjpddcFH3CYqBtausqAa3qk1ehSGn1wUxiH4solbGnVz2nCHBkSbjKUTsnXzvUJv-3ngvy9uV5Xd8X9w-3vanlfeCH0VJTaK6FEw1rOtDbKiVIC85yha4xG2UkJQqEA04Bo0PDWW186ox003jJZLsiPXe9zHF9mTFO9Cclj37sBxznVxlhRKsbs56QywLUpVSa_fyCfxjkO-YwMabHltnV8B_k4phSxq59j2Lj4WjOot6bq_6bqral6byqHLvbNc7PB9j3ypiYD5zsgIOL7t1RcWmbKf9uLlZQ</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Wang, Tsaipei</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>201106</creationdate><title>CA-Tree: A Hierarchical Structure for Efficient and Scalable Coassociation-Based Cluster Ensembles</title><author>Wang, Tsaipei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-37c6464b1d217786a43501c21eab87e5f55046e408b04be82dc9c3a87a0bc9153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Buildings</topic><topic>Cluster Analysis</topic><topic>Cluster ensemble</topic><topic>Clustering algorithms</topic><topic>Clusters</topic><topic>coassociation matrix</topic><topic>Complexity</topic><topic>Complexity theory</topic><topic>Computer Simulation</topic><topic>Cybernetics</topic><topic>Data structures</topic><topic>Decision Support Techniques</topic><topic>Diversity reception</topic><topic>Mathematical analysis</topic><topic>Matrices</topic><topic>Matrix methods</topic><topic>Models, Theoretical</topic><topic>multiple clusterings</topic><topic>Partitioning algorithms</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Prototypes</topic><topic>Upper bound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Tsaipei</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Tsaipei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CA-Tree: A Hierarchical Structure for Efficient and Scalable Coassociation-Based Cluster Ensembles</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TSMCB</stitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><date>2011-06</date><risdate>2011</risdate><volume>41</volume><issue>3</issue><spage>686</spage><epage>698</epage><pages>686-698</pages><issn>1083-4419</issn><issn>2168-2267</issn><eissn>1941-0492</eissn><eissn>2168-2275</eissn><coden>ITSCFI</coden><abstract>Cluster ensembles have attracted a lot of research interests in recent years, and their applications continue to expand. Among the various algorithms for cluster ensembles, those based on coassociation matrices are probably the ones studied and used the most because coassociation matrices are easy to understand and implement. However, the main limitation of coassociation matrices as the data structure for combining multiple clusterings is the complexity that is at least quadratic to the number of patterns N . In this paper, we propose CA-tree, which is a dendogram-like hierarchical data structure, to facilitate efficient and scalable cluster ensembles for coassociation-matrix-based algorithms. All the properties of the CA-tree are derived from base cluster labels and do not require the access to the original data features. We then apply a threshold to the CA-tree to obtain a set of nodes, which are then used in place of the original patterns for ensemble-clustering algorithms. The experiments demonstrate that the complexity for coassociation-based cluster ensembles can be reduced to close to linear to N with minimal loss on clustering accuracy.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>21075727</pmid><doi>10.1109/TSMCB.2010.2086059</doi><tpages>13</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1083-4419 |
| ispartof | IEEE transactions on cybernetics, 2011-06, Vol.41 (3), p.686-698 |
| issn | 1083-4419 2168-2267 1941-0492 2168-2275 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_889436119 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Artificial Intelligence Buildings Cluster Analysis Cluster ensemble Clustering algorithms Clusters coassociation matrix Complexity Complexity theory Computer Simulation Cybernetics Data structures Decision Support Techniques Diversity reception Mathematical analysis Matrices Matrix methods Models, Theoretical multiple clusterings Partitioning algorithms Pattern Recognition, Automated - methods Prototypes Upper bound |
| title | CA-Tree: A Hierarchical Structure for Efficient and Scalable Coassociation-Based Cluster Ensembles |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T02%3A38%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=CA-Tree:%20A%20Hierarchical%20Structure%20for%20Efficient%20and%20Scalable%20Coassociation-Based%20Cluster%20Ensembles&rft.jtitle=IEEE%20transactions%20on%20cybernetics&rft.au=Wang,%20Tsaipei&rft.date=2011-06&rft.volume=41&rft.issue=3&rft.spage=686&rft.epage=698&rft.pages=686-698&rft.issn=1083-4419&rft.eissn=1941-0492&rft.coden=ITSCFI&rft_id=info:doi/10.1109/TSMCB.2010.2086059&rft_dat=%3Cproquest_RIE%3E889436119%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=867468029&rft_id=info:pmid/21075727&rft_ieee_id=5625918&rfr_iscdi=true |