Finding input sub-spaces for Polymorphic Fuzzy Signatures
A significant feature of fuzzy signatures is its applicability for complex and sparse data. To create polymorphic fuzzy signatures (PFS) for sparse data, sparse input sub-spaces (ISSs) should be considered. Finding the optimal ISSs manually is not a simple task as it is time consuming; moreover, som...
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 | 1094 |
|---|---|
| container_issue | |
| container_start_page | 1089 |
| container_title | |
| container_volume | |
| creator | Hadad, A.H. Gedeon, T.D. Mendis, B.S.U. |
| description | A significant feature of fuzzy signatures is its applicability for complex and sparse data. To create polymorphic fuzzy signatures (PFS) for sparse data, sparse input sub-spaces (ISSs) should be considered. Finding the optimal ISSs manually is not a simple task as it is time consuming; moreover, some knowledge about the dataset is necessary. Fuzzy c-means (FCM) clustering employed with a trapezoidal approximation method is needed to find ISSs automatically. Furthermore, dealing with sparse data, we should be mindful about choosing a reliable trapezoidal approximation method. This facilitates the optimal ISS creation for the data. In our experiment, two trapezoidal approximation methods were used to find optimal ISSs. The results demonstrate that our version of trapezoidal approximation for creating ISSs result in an PFS with lower mean square error compared to the original trapezoidal approximation method. |
| doi_str_mv | 10.1109/FUZZY.2009.5277055 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5277055</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5277055</ieee_id><sourcerecordid>5277055</sourcerecordid><originalsourceid>FETCH-LOGICAL-i219t-363be749e13aee7b4ea5cbf77c4ee47ee2a720cd487ce3418f2dcf5c8a2bf97b3</originalsourceid><addsrcrecordid>eNpVj81Kw0AUhUdUsNS8gG7yAonz2zuzlGJUKChoF3ZTZiZ36kibhEyySJ_egt14NodvcT44hNwxWjJGzUO13my-Sk6pKRUHoEpdkMyAZpJLKZQBdfmPF_qKzE5DXYDS8oZkKf3QU6QSTLAZMVVs6tjs8th045Cn0RWpsx5THto-f2_306Htu-_o82o8Hqf8I-4aO4w9pltyHew-YXbuOVlXT5_Ll2L19vy6fFwVkTMzFGIhHII0yIRFBCfRKu8CgJeIEhC5BU59LTV4FJLpwGsflNeWu2DAiTm5__NGRNx2fTzYftqez4tfNbJMgQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Finding input sub-spaces for Polymorphic Fuzzy Signatures</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Hadad, A.H. ; Gedeon, T.D. ; Mendis, B.S.U.</creator><creatorcontrib>Hadad, A.H. ; Gedeon, T.D. ; Mendis, B.S.U.</creatorcontrib><description>A significant feature of fuzzy signatures is its applicability for complex and sparse data. To create polymorphic fuzzy signatures (PFS) for sparse data, sparse input sub-spaces (ISSs) should be considered. Finding the optimal ISSs manually is not a simple task as it is time consuming; moreover, some knowledge about the dataset is necessary. Fuzzy c-means (FCM) clustering employed with a trapezoidal approximation method is needed to find ISSs automatically. Furthermore, dealing with sparse data, we should be mindful about choosing a reliable trapezoidal approximation method. This facilitates the optimal ISS creation for the data. In our experiment, two trapezoidal approximation methods were used to find optimal ISSs. The results demonstrate that our version of trapezoidal approximation for creating ISSs result in an PFS with lower mean square error compared to the original trapezoidal approximation method.</description><identifier>ISSN: 1098-7584</identifier><identifier>ISBN: 9781424435968</identifier><identifier>ISBN: 142443596X</identifier><identifier>EISBN: 9781424435975</identifier><identifier>EISBN: 1424435978</identifier><identifier>DOI: 10.1109/FUZZY.2009.5277055</identifier><language>eng</language><publisher>IEEE</publisher><subject>Approximation methods ; Computer science ; Data mining ; Feedback ; Fellows ; Fuzzy C-Means ; Fuzzy sets ; Fuzzy Signatures ; Input subspace clustering ; Mean square error methods ; Optimization methods ; Polymorphic Fuzzy Signatures ; Remuneration ; Skeleton ; Trapezoidal Approximation ; WRAO</subject><ispartof>2009 IEEE International Conference on Fuzzy Systems, 2009, p.1089-1094</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5277055$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5277055$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hadad, A.H.</creatorcontrib><creatorcontrib>Gedeon, T.D.</creatorcontrib><creatorcontrib>Mendis, B.S.U.</creatorcontrib><title>Finding input sub-spaces for Polymorphic Fuzzy Signatures</title><title>2009 IEEE International Conference on Fuzzy Systems</title><addtitle>FUZZY</addtitle><description>A significant feature of fuzzy signatures is its applicability for complex and sparse data. To create polymorphic fuzzy signatures (PFS) for sparse data, sparse input sub-spaces (ISSs) should be considered. Finding the optimal ISSs manually is not a simple task as it is time consuming; moreover, some knowledge about the dataset is necessary. Fuzzy c-means (FCM) clustering employed with a trapezoidal approximation method is needed to find ISSs automatically. Furthermore, dealing with sparse data, we should be mindful about choosing a reliable trapezoidal approximation method. This facilitates the optimal ISS creation for the data. In our experiment, two trapezoidal approximation methods were used to find optimal ISSs. The results demonstrate that our version of trapezoidal approximation for creating ISSs result in an PFS with lower mean square error compared to the original trapezoidal approximation method.</description><subject>Approximation methods</subject><subject>Computer science</subject><subject>Data mining</subject><subject>Feedback</subject><subject>Fellows</subject><subject>Fuzzy C-Means</subject><subject>Fuzzy sets</subject><subject>Fuzzy Signatures</subject><subject>Input subspace clustering</subject><subject>Mean square error methods</subject><subject>Optimization methods</subject><subject>Polymorphic Fuzzy Signatures</subject><subject>Remuneration</subject><subject>Skeleton</subject><subject>Trapezoidal Approximation</subject><subject>WRAO</subject><issn>1098-7584</issn><isbn>9781424435968</isbn><isbn>142443596X</isbn><isbn>9781424435975</isbn><isbn>1424435978</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVj81Kw0AUhUdUsNS8gG7yAonz2zuzlGJUKChoF3ZTZiZ36kibhEyySJ_egt14NodvcT44hNwxWjJGzUO13my-Sk6pKRUHoEpdkMyAZpJLKZQBdfmPF_qKzE5DXYDS8oZkKf3QU6QSTLAZMVVs6tjs8th045Cn0RWpsx5THto-f2_306Htu-_o82o8Hqf8I-4aO4w9pltyHew-YXbuOVlXT5_Ll2L19vy6fFwVkTMzFGIhHII0yIRFBCfRKu8CgJeIEhC5BU59LTV4FJLpwGsflNeWu2DAiTm5__NGRNx2fTzYftqez4tfNbJMgQ</recordid><startdate>200908</startdate><enddate>200908</enddate><creator>Hadad, A.H.</creator><creator>Gedeon, T.D.</creator><creator>Mendis, B.S.U.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200908</creationdate><title>Finding input sub-spaces for Polymorphic Fuzzy Signatures</title><author>Hadad, A.H. ; Gedeon, T.D. ; Mendis, B.S.U.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i219t-363be749e13aee7b4ea5cbf77c4ee47ee2a720cd487ce3418f2dcf5c8a2bf97b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Approximation methods</topic><topic>Computer science</topic><topic>Data mining</topic><topic>Feedback</topic><topic>Fellows</topic><topic>Fuzzy C-Means</topic><topic>Fuzzy sets</topic><topic>Fuzzy Signatures</topic><topic>Input subspace clustering</topic><topic>Mean square error methods</topic><topic>Optimization methods</topic><topic>Polymorphic Fuzzy Signatures</topic><topic>Remuneration</topic><topic>Skeleton</topic><topic>Trapezoidal Approximation</topic><topic>WRAO</topic><toplevel>online_resources</toplevel><creatorcontrib>Hadad, A.H.</creatorcontrib><creatorcontrib>Gedeon, T.D.</creatorcontrib><creatorcontrib>Mendis, B.S.U.</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>Hadad, A.H.</au><au>Gedeon, T.D.</au><au>Mendis, B.S.U.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Finding input sub-spaces for Polymorphic Fuzzy Signatures</atitle><btitle>2009 IEEE International Conference on Fuzzy Systems</btitle><stitle>FUZZY</stitle><date>2009-08</date><risdate>2009</risdate><spage>1089</spage><epage>1094</epage><pages>1089-1094</pages><issn>1098-7584</issn><isbn>9781424435968</isbn><isbn>142443596X</isbn><eisbn>9781424435975</eisbn><eisbn>1424435978</eisbn><abstract>A significant feature of fuzzy signatures is its applicability for complex and sparse data. To create polymorphic fuzzy signatures (PFS) for sparse data, sparse input sub-spaces (ISSs) should be considered. Finding the optimal ISSs manually is not a simple task as it is time consuming; moreover, some knowledge about the dataset is necessary. Fuzzy c-means (FCM) clustering employed with a trapezoidal approximation method is needed to find ISSs automatically. Furthermore, dealing with sparse data, we should be mindful about choosing a reliable trapezoidal approximation method. This facilitates the optimal ISS creation for the data. In our experiment, two trapezoidal approximation methods were used to find optimal ISSs. The results demonstrate that our version of trapezoidal approximation for creating ISSs result in an PFS with lower mean square error compared to the original trapezoidal approximation method.</abstract><pub>IEEE</pub><doi>10.1109/FUZZY.2009.5277055</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1098-7584 |
| ispartof | 2009 IEEE International Conference on Fuzzy Systems, 2009, p.1089-1094 |
| issn | 1098-7584 |
| language | eng |
| recordid | cdi_ieee_primary_5277055 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation methods Computer science Data mining Feedback Fellows Fuzzy C-Means Fuzzy sets Fuzzy Signatures Input subspace clustering Mean square error methods Optimization methods Polymorphic Fuzzy Signatures Remuneration Skeleton Trapezoidal Approximation WRAO |
| title | Finding input sub-spaces for Polymorphic Fuzzy Signatures |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T01%3A06%3A08IST&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=Finding%20input%20sub-spaces%20for%20Polymorphic%20Fuzzy%20Signatures&rft.btitle=2009%20IEEE%20International%20Conference%20on%20Fuzzy%20Systems&rft.au=Hadad,%20A.H.&rft.date=2009-08&rft.spage=1089&rft.epage=1094&rft.pages=1089-1094&rft.issn=1098-7584&rft.isbn=9781424435968&rft.isbn_list=142443596X&rft_id=info:doi/10.1109/FUZZY.2009.5277055&rft_dat=%3Cieee_6IE%3E5277055%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424435975&rft.eisbn_list=1424435978&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5277055&rfr_iscdi=true |