Outliers influence to the point distance distribution normality within the data clusters
In order to verify the cluster analysis results, a normality test is being applied to the distribution of data point's distances from their cluster center. The presence of the outlier points within the input data can however influence this method in a negative way. Therefore, a normality test w...
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| creator | Malkic, J. Sarajlic, N. Hadzic, D. |
| description | In order to verify the cluster analysis results, a normality test is being applied to the distribution of data point's distances from their cluster center. The presence of the outlier points within the input data can however influence this method in a negative way. Therefore, a normality test will show better results in recognizing and assessing the clusters if the outlier presence is reduced. This fact is being confirmed by empirically comparing the normality test results for the clusters produced by different cluster analyses methods on the same data set. |
| doi_str_mv | 10.1109/TELFOR.2012.6419542 |
| format | Conference Proceeding |
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The presence of the outlier points within the input data can however influence this method in a negative way. Therefore, a normality test will show better results in recognizing and assessing the clusters if the outlier presence is reduced. This fact is being confirmed by empirically comparing the normality test results for the clusters produced by different cluster analyses methods on the same data set.</description><identifier>ISBN: 9781467329835</identifier><identifier>ISBN: 1467329835</identifier><identifier>EISBN: 9781467329842</identifier><identifier>EISBN: 9781467329828</identifier><identifier>EISBN: 1467329827</identifier><identifier>EISBN: 1467329843</identifier><identifier>DOI: 10.1109/TELFOR.2012.6419542</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Cluster verification ; Clustering algorithms ; data mining ; distance distribution normality ; Gaussian distribution ; Histograms ; Shape ; Vectors</subject><ispartof>2012 20th Telecommunications Forum (TELFOR), 2012, p.1653-1656</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6419542$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6419542$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Malkic, J.</creatorcontrib><creatorcontrib>Sarajlic, N.</creatorcontrib><creatorcontrib>Hadzic, D.</creatorcontrib><title>Outliers influence to the point distance distribution normality within the data clusters</title><title>2012 20th Telecommunications Forum (TELFOR)</title><addtitle>TELFOR</addtitle><description>In order to verify the cluster analysis results, a normality test is being applied to the distribution of data point's distances from their cluster center. The presence of the outlier points within the input data can however influence this method in a negative way. Therefore, a normality test will show better results in recognizing and assessing the clusters if the outlier presence is reduced. This fact is being confirmed by empirically comparing the normality test results for the clusters produced by different cluster analyses methods on the same data set.</description><subject>Algorithm design and analysis</subject><subject>Cluster verification</subject><subject>Clustering algorithms</subject><subject>data mining</subject><subject>distance distribution normality</subject><subject>Gaussian distribution</subject><subject>Histograms</subject><subject>Shape</subject><subject>Vectors</subject><isbn>9781467329835</isbn><isbn>1467329835</isbn><isbn>9781467329842</isbn><isbn>9781467329828</isbn><isbn>1467329827</isbn><isbn>1467329843</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkMtKw0AYhUdEUGqeoJt5gcR_LpnLUkqrQiAgFdyVyVzoSDopyQTp29tqN67OBc63OAgtCVSEgH7arptN-15RILQSnOia0xtUaKkIF5JRrTi9_ZdZfY-KafoCgDNAguAP6LOdcx_9OOGYQj_7ZD3OA857j49DTBm7OGVzaS9mjN2c45BwGsaD6WM-4e-Y9zH9DpzJBtt-nvKZ94juguknX1x1gT426-3qtWzal7fVc1NGIutcCt6BV5YyAVYIp5mBDihQpwOHLgTQnVZCSeeUVDW3jAD31jobqPQeFFug5R83eu93xzEezHjaXf9gP5nOVaU</recordid><startdate>201211</startdate><enddate>201211</enddate><creator>Malkic, J.</creator><creator>Sarajlic, N.</creator><creator>Hadzic, D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201211</creationdate><title>Outliers influence to the point distance distribution normality within the data clusters</title><author>Malkic, J. ; Sarajlic, N. ; Hadzic, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-64b0e8c2360c66d93a0b0202d9f40bff09b98687dd87854c3104eccdcf27ee083</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithm design and analysis</topic><topic>Cluster verification</topic><topic>Clustering algorithms</topic><topic>data mining</topic><topic>distance distribution normality</topic><topic>Gaussian distribution</topic><topic>Histograms</topic><topic>Shape</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Malkic, J.</creatorcontrib><creatorcontrib>Sarajlic, N.</creatorcontrib><creatorcontrib>Hadzic, D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Malkic, J.</au><au>Sarajlic, N.</au><au>Hadzic, D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Outliers influence to the point distance distribution normality within the data clusters</atitle><btitle>2012 20th Telecommunications Forum (TELFOR)</btitle><stitle>TELFOR</stitle><date>2012-11</date><risdate>2012</risdate><spage>1653</spage><epage>1656</epage><pages>1653-1656</pages><isbn>9781467329835</isbn><isbn>1467329835</isbn><eisbn>9781467329842</eisbn><eisbn>9781467329828</eisbn><eisbn>1467329827</eisbn><eisbn>1467329843</eisbn><abstract>In order to verify the cluster analysis results, a normality test is being applied to the distribution of data point's distances from their cluster center. The presence of the outlier points within the input data can however influence this method in a negative way. Therefore, a normality test will show better results in recognizing and assessing the clusters if the outlier presence is reduced. This fact is being confirmed by empirically comparing the normality test results for the clusters produced by different cluster analyses methods on the same data set.</abstract><pub>IEEE</pub><doi>10.1109/TELFOR.2012.6419542</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Cluster verification Clustering algorithms data mining distance distribution normality Gaussian distribution Histograms Shape Vectors |
| title | Outliers influence to the point distance distribution normality within the data clusters |
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