Supervised fuzzy clustering for rule extraction
The paper is concerned with the application of orthogonal transforms and fuzzy clustering to the extraction of fuzzy rules from data. It is proposed to use the orthogonal least squares method to supervise the progress of the fuzzy clustering algorithm and remove clusters of less importance with resp...
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| creator | Setnes, M. |
| description | The paper is concerned with the application of orthogonal transforms and fuzzy clustering to the extraction of fuzzy rules from data. It is proposed to use the orthogonal least squares method to supervise the progress of the fuzzy clustering algorithm and remove clusters of less importance with respect to fitting the data. Clustering takes place in the product space of systems in and outputs, and each cluster corresponds to a fuzzy IF-THEN rule. By initializing the clustering with an overestimated number of clusters, and subsequently remove less important clusters (rules) as the clustering progresses, it is sought to obtain a suitable partition of the data in an automated. The approach is studied for the fuzzy c-means algorithm and applied to a function approximation example known from the literature. |
| doi_str_mv | 10.1109/FUZZY.1999.790084 |
| format | Conference Proceeding |
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The approach is studied for the fuzzy c-means algorithm and applied to a function approximation example known from the literature.</description><subject>Approximation algorithms</subject><subject>Clustering algorithms</subject><subject>Data mining</subject><subject>Fuzzy systems</subject><subject>Humans</subject><subject>Laboratories</subject><subject>Linear regression</subject><subject>Partitioning algorithms</subject><subject>Space technology</subject><subject>State-space methods</subject><issn>1098-7584</issn><isbn>9780780354067</isbn><isbn>0780354060</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1999</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT8FKw0AUXFDB0uYD9JQfSPpedrP79ijFqlDwYHuwl7JJ3spKbMsmEduvN1iHgbnMDDNC3CHkiGDny812-56jtTY3FoDUlUisIRgpSwXaXIvJ6KPMlKRuRdJ1nzDCKlSlmYj523Dk-B06blI_nM-ntG6HrucY9h-pP8Q0Di2n_NNHV_fhsJ-JG-_ajpN_nYrN8nG9eM5Wr08vi4dVFlCCytCYCqTR7F1VGQ9SM7PloqnrShFp0IiEhabGV6742wOeHZZE0nvyciruL71hDO6OMXy5eNpdHspffFpEtg</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Setnes, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>1999</creationdate><title>Supervised fuzzy clustering for rule extraction</title><author>Setnes, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1304-177b0376efabb7f036eee9e2dccb4886061181268dfba2000940fea15883ff8f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Approximation algorithms</topic><topic>Clustering algorithms</topic><topic>Data mining</topic><topic>Fuzzy systems</topic><topic>Humans</topic><topic>Laboratories</topic><topic>Linear regression</topic><topic>Partitioning algorithms</topic><topic>Space technology</topic><topic>State-space methods</topic><toplevel>online_resources</toplevel><creatorcontrib>Setnes, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Setnes, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Supervised fuzzy clustering for rule extraction</atitle><btitle>FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315)</btitle><stitle>FUZZY</stitle><date>1999</date><risdate>1999</risdate><volume>3</volume><spage>1270</spage><epage>1274 vol.3</epage><pages>1270-1274 vol.3</pages><issn>1098-7584</issn><isbn>9780780354067</isbn><isbn>0780354060</isbn><abstract>The paper is concerned with the application of orthogonal transforms and fuzzy clustering to the extraction of fuzzy rules from data. It is proposed to use the orthogonal least squares method to supervise the progress of the fuzzy clustering algorithm and remove clusters of less importance with respect to fitting the data. Clustering takes place in the product space of systems in and outputs, and each cluster corresponds to a fuzzy IF-THEN rule. By initializing the clustering with an overestimated number of clusters, and subsequently remove less important clusters (rules) as the clustering progresses, it is sought to obtain a suitable partition of the data in an automated. 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| identifier | ISSN: 1098-7584 |
| ispartof | FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315), 1999, Vol.3, p.1270-1274 vol.3 |
| issn | 1098-7584 |
| language | eng |
| recordid | cdi_ieee_primary_790084 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation algorithms Clustering algorithms Data mining Fuzzy systems Humans Laboratories Linear regression Partitioning algorithms Space technology State-space methods |
| title | Supervised fuzzy clustering for rule extraction |
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