Outlier Detection Using Rough Set Theory
In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formall...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 87 |
|---|---|
| container_issue | |
| container_start_page | 79 |
| container_title | |
| container_volume | |
| creator | Jiang, Feng Sui, Yuefei Cao, Cungen |
| description | In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special cases of exceptional set and minimal exceptional set. Finally, we introduce a new definition for outliers as well as the definition of exceptional degree. Through calculating the exceptional degree for each object in minimal exceptional sets, we can find out all outliers in a given dataset. |
| doi_str_mv | 10.1007/11548706_9 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_17182553</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>17182553</sourcerecordid><originalsourceid>FETCH-LOGICAL-p218t-ec7ee33c096766ab827329a02489df7fb24fea9724d89f4fba2b4efc67e0e5b73</originalsourceid><addsrcrecordid>eNpFkMtOwzAURM1LopRu-IJskLoJXD_iay9Ry0uqVAnateWk120gJFGcLvr3BBXBahZzNJoZxm443HEAvOc8UwZBO3vCJhaNzBRIboQyp2zENeeplMqe_XnCaA3mnI1AgkgtKnnJrmL8AACBVozYdLnvq5K6ZE49FX3Z1Mk6lvU2eWv2213yTn2y2lHTHa7ZRfBVpMmvjtn66XE1e0kXy-fX2cMibQU3fUoFEklZgNWotc-NQCmsh6Gh3QQMuVCBvEWhNsYGFXIvckWh0EhAWY5yzG6Pua2Pha9C5-uijK7tyi_fHRzHYW2WyYGbHrk4WPWWOpc3zWd0HNzPU-7_KfkNHsFURg</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Outlier Detection Using Rough Set Theory</title><source>Springer Books</source><creator>Jiang, Feng ; Sui, Yuefei ; Cao, Cungen</creator><contributor>Yao, JingTao ; Ziarko, Wojciech ; Ślęzak, Dominik ; Peters, James F. ; Hu, Xiaohua</contributor><creatorcontrib>Jiang, Feng ; Sui, Yuefei ; Cao, Cungen ; Yao, JingTao ; Ziarko, Wojciech ; Ślęzak, Dominik ; Peters, James F. ; Hu, Xiaohua</creatorcontrib><description>In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special cases of exceptional set and minimal exceptional set. Finally, we introduce a new definition for outliers as well as the definition of exceptional degree. Through calculating the exceptional degree for each object in minimal exceptional sets, we can find out all outliers in a given dataset.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540286608</identifier><identifier>ISBN: 3540286608</identifier><identifier>ISBN: 3540286535</identifier><identifier>ISBN: 9783540286530</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540318248</identifier><identifier>EISBN: 3540318240</identifier><identifier>DOI: 10.1007/11548706_9</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Computer science; control theory; systems ; Exact sciences and technology ; Theoretical computing</subject><ispartof>Lecture notes in computer science, 2005, p.79-87</ispartof><rights>Springer-Verlag Berlin Heidelberg 2005</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11548706_9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11548706_9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,4036,4037,27902,38232,41418,42487</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17182553$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Yao, JingTao</contributor><contributor>Ziarko, Wojciech</contributor><contributor>Ślęzak, Dominik</contributor><contributor>Peters, James F.</contributor><contributor>Hu, Xiaohua</contributor><creatorcontrib>Jiang, Feng</creatorcontrib><creatorcontrib>Sui, Yuefei</creatorcontrib><creatorcontrib>Cao, Cungen</creatorcontrib><title>Outlier Detection Using Rough Set Theory</title><title>Lecture notes in computer science</title><description>In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special cases of exceptional set and minimal exceptional set. Finally, we introduce a new definition for outliers as well as the definition of exceptional degree. Through calculating the exceptional degree for each object in minimal exceptional sets, we can find out all outliers in a given dataset.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Theoretical computing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540286608</isbn><isbn>3540286608</isbn><isbn>3540286535</isbn><isbn>9783540286530</isbn><isbn>9783540318248</isbn><isbn>3540318240</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpFkMtOwzAURM1LopRu-IJskLoJXD_iay9Ry0uqVAnateWk120gJFGcLvr3BBXBahZzNJoZxm443HEAvOc8UwZBO3vCJhaNzBRIboQyp2zENeeplMqe_XnCaA3mnI1AgkgtKnnJrmL8AACBVozYdLnvq5K6ZE49FX3Z1Mk6lvU2eWv2213yTn2y2lHTHa7ZRfBVpMmvjtn66XE1e0kXy-fX2cMibQU3fUoFEklZgNWotc-NQCmsh6Gh3QQMuVCBvEWhNsYGFXIvckWh0EhAWY5yzG6Pua2Pha9C5-uijK7tyi_fHRzHYW2WyYGbHrk4WPWWOpc3zWd0HNzPU-7_KfkNHsFURg</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Jiang, Feng</creator><creator>Sui, Yuefei</creator><creator>Cao, Cungen</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Outlier Detection Using Rough Set Theory</title><author>Jiang, Feng ; Sui, Yuefei ; Cao, Cungen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p218t-ec7ee33c096766ab827329a02489df7fb24fea9724d89f4fba2b4efc67e0e5b73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Feng</creatorcontrib><creatorcontrib>Sui, Yuefei</creatorcontrib><creatorcontrib>Cao, Cungen</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Feng</au><au>Sui, Yuefei</au><au>Cao, Cungen</au><au>Yao, JingTao</au><au>Ziarko, Wojciech</au><au>Ślęzak, Dominik</au><au>Peters, James F.</au><au>Hu, Xiaohua</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Outlier Detection Using Rough Set Theory</atitle><btitle>Lecture notes in computer science</btitle><date>2005</date><risdate>2005</risdate><spage>79</spage><epage>87</epage><pages>79-87</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540286608</isbn><isbn>3540286608</isbn><isbn>3540286535</isbn><isbn>9783540286530</isbn><eisbn>9783540318248</eisbn><eisbn>3540318240</eisbn><abstract>In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special cases of exceptional set and minimal exceptional set. Finally, we introduce a new definition for outliers as well as the definition of exceptional degree. Through calculating the exceptional degree for each object in minimal exceptional sets, we can find out all outliers in a given dataset.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11548706_9</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2005, p.79-87 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17182553 |
| source | Springer Books |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Computer science control theory systems Exact sciences and technology Theoretical computing |
| title | Outlier Detection Using Rough Set Theory |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T11%3A43%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Outlier%20Detection%20Using%20Rough%20Set%20Theory&rft.btitle=Lecture%20notes%20in%20computer%20science&rft.au=Jiang,%20Feng&rft.date=2005&rft.spage=79&rft.epage=87&rft.pages=79-87&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=9783540286608&rft.isbn_list=3540286608&rft.isbn_list=3540286535&rft.isbn_list=9783540286530&rft_id=info:doi/10.1007/11548706_9&rft_dat=%3Cpascalfrancis_sprin%3E17182553%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=9783540318248&rft.eisbn_list=3540318240&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |