Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances
This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their represe...
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| Veröffentlicht in: | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2009-11, Vol.39 (6), p.1295-1306 |
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| container_title | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans |
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| creator | de A.T. de Carvalho, F. Lechevallier, Y. |
| description | This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools. |
| doi_str_mv | 10.1109/TSMCA.2009.2030167 |
| format | Article |
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These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.</description><identifier>ISSN: 1083-4427</identifier><identifier>ISSN: 2168-2216</identifier><identifier>EISSN: 1558-2426</identifier><identifier>EISSN: 2168-2232</identifier><identifier>DOI: 10.1109/TSMCA.2009.2030167</identifier><identifier>CODEN: ITSHFX</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive quadratic distances ; cluster interpretation indexes ; Clustering algorithms ; clustering analysis ; Clustering methods ; Data analysis ; Data mining ; Heuristic algorithms ; Iterative algorithms ; Optimization methods ; partition interpretation indexes ; Partitioning algorithms ; Pattern recognition ; Prototypes ; symbolic interval data analysis</subject><ispartof>IEEE transactions on systems, man and cybernetics. 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(IEEE) 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c326t-47159ba447a2d881d2d085d419605b25e6265709cf8a6e86663f1492729e4e133</citedby><cites>FETCH-LOGICAL-c326t-47159ba447a2d881d2d085d419605b25e6265709cf8a6e86663f1492729e4e133</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5281204$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27922,27923,54756</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5281204$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>de A.T. de Carvalho, F.</creatorcontrib><creatorcontrib>Lechevallier, Y.</creatorcontrib><title>Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances</title><title>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</title><addtitle>TSMCA</addtitle><description>This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.</description><subject>Adaptive quadratic distances</subject><subject>cluster interpretation indexes</subject><subject>Clustering algorithms</subject><subject>clustering analysis</subject><subject>Clustering methods</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Heuristic algorithms</subject><subject>Iterative algorithms</subject><subject>Optimization methods</subject><subject>partition interpretation indexes</subject><subject>Partitioning algorithms</subject><subject>Pattern recognition</subject><subject>Prototypes</subject><subject>symbolic interval data analysis</subject><issn>1083-4427</issn><issn>2168-2216</issn><issn>1558-2426</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkLtOw0AQRVcIJELgB6CxaKgc9v0og8MjUhAgAu1qYq-RI8cOu3ak_D0bElHQzNzi3NHoIHRJ8IgQbG7n78_ZeEQxNnEwTKQ6QgMihE4pp_I4ZqxZyjlVp-gshCXGhHPDB-h1sm1gVeVJVvehc75qvpK2TKZNzBuo00-oe1ckE-gguYMQY9sk4wLWXbVxyVsPhYcu1idV6KDJXThHJyXUwV0c9hB9PNzPs6d09vI4zcazNGdUdilXRJgFcK6AFlqTghZYi4ITI7FYUOEklUJhk5capNNSSlYSbqiixnFHGBuim_3dtW-_exc6u6pC7uoaGtf2wWqpFBPU6Ehe_yOXbe-b-JzVQnEWdeEI0T2U-zYE70q79tUK_NYSbHeK7a9iu1NsD4pj6WpfqpxzfwVBNaGYsx_G2XVG</recordid><startdate>20091101</startdate><enddate>20091101</enddate><creator>de A.T. de Carvalho, F.</creator><creator>Lechevallier, Y.</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>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7U1</scope><scope>7U2</scope><scope>C1K</scope></search><sort><creationdate>20091101</creationdate><title>Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances</title><author>de A.T. de Carvalho, F. ; Lechevallier, Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-47159ba447a2d881d2d085d419605b25e6265709cf8a6e86663f1492729e4e133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adaptive quadratic distances</topic><topic>cluster interpretation indexes</topic><topic>Clustering algorithms</topic><topic>clustering analysis</topic><topic>Clustering methods</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Heuristic algorithms</topic><topic>Iterative algorithms</topic><topic>Optimization methods</topic><topic>partition interpretation indexes</topic><topic>Partitioning algorithms</topic><topic>Pattern recognition</topic><topic>Prototypes</topic><topic>symbolic interval data analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de A.T. de Carvalho, F.</creatorcontrib><creatorcontrib>Lechevallier, Y.</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>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>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>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>de A.T. de Carvalho, F.</au><au>Lechevallier, Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances</atitle><jtitle>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</jtitle><stitle>TSMCA</stitle><date>2009-11-01</date><risdate>2009</risdate><volume>39</volume><issue>6</issue><spage>1295</spage><epage>1306</epage><pages>1295-1306</pages><issn>1083-4427</issn><issn>2168-2216</issn><eissn>1558-2426</eissn><eissn>2168-2232</eissn><coden>ITSHFX</coden><abstract>This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSMCA.2009.2030167</doi><tpages>12</tpages></addata></record> |
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| ispartof | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans, 2009-11, Vol.39 (6), p.1295-1306 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive quadratic distances cluster interpretation indexes Clustering algorithms clustering analysis Clustering methods Data analysis Data mining Heuristic algorithms Iterative algorithms Optimization methods partition interpretation indexes Partitioning algorithms Pattern recognition Prototypes symbolic interval data analysis |
| title | Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances |
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