A Clustering Algorithm Based on Discretized Interval Value
In order to improve the quality of traditional clustering algorithm and prevent the distribution of data from affecting the clustering algorithm greatly, a clustering algorithm based on interval value was proposed. Depending on the consistency of condition attributes and decision attributes in the d...
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| creator | Xu, E. Liangshan Shao Wendong Tan |
| description | In order to improve the quality of traditional clustering algorithm and prevent the distribution of data from affecting the clustering algorithm greatly, a clustering algorithm based on interval value was proposed. Depending on the consistency of condition attributes and decision attributes in the decision table, the data was discretized and attributes were reduced by using data super-cube and information entropy. Based on the above, the algorithm can use the additivity of set feature vector to cluster data just by scanning the decision table only one time. Experimental results indicate that the algorithm is efficient and effective |
| doi_str_mv | 10.1109/CESA.2006.4281773 |
| format | Conference Proceeding |
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Experimental results indicate that the algorithm is efficient and effective</description><subject>Application software</subject><subject>clustering</subject><subject>Clustering algorithms</subject><subject>Constraint theory</subject><subject>decision table</subject><subject>discretization</subject><subject>Information entropy</subject><subject>Information systems</subject><subject>rough set</subject><subject>set feature vector</subject><subject>Set theory</subject><subject>Space exploration</subject><subject>Space technology</subject><subject>Systems engineering and theory</subject><subject>Systems engineering education</subject><isbn>7302139229</isbn><isbn>9787302139225</isbn><isbn>7900718141</isbn><isbn>9787900718143</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tKw0AYRkdEUGsfQNzMCyT-_0zm5i7GqoWCCy_bMrfUkTSVTCrYpzdgV4cDhw8-Qq4RSkQwt83itS4ZgCwrplEpfkIulQFQqLHC00k4MOSGMXNO5jl_AQAaaVCKC3JX06bb5zEOqd_QutvshjR-bum9zTHQXU8fUvZDHNNh0mU_dT-2ox-228crctbaLsf5kTPy_rh4a56L1cvTsqlXRUIQYyGMlKIFa5wXWkMVWqEqqTECCHBBe7QYMHIVXRAIPDjFnNK-iuC5Z47PyM3_booxrr-HtLXD7_r4lf8BmTRG3g</recordid><startdate>200610</startdate><enddate>200610</enddate><creator>Xu, E.</creator><creator>Liangshan Shao</creator><creator>Wendong Tan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200610</creationdate><title>A Clustering Algorithm Based on Discretized Interval Value</title><author>Xu, E. ; Liangshan Shao ; Wendong Tan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i105t-59665f0a9bc58804df574681e0050bd8c1a1d1e37ebd5103db72b78c4e0c3c2b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Application software</topic><topic>clustering</topic><topic>Clustering algorithms</topic><topic>Constraint theory</topic><topic>decision table</topic><topic>discretization</topic><topic>Information entropy</topic><topic>Information systems</topic><topic>rough set</topic><topic>set feature vector</topic><topic>Set theory</topic><topic>Space exploration</topic><topic>Space technology</topic><topic>Systems engineering and theory</topic><topic>Systems engineering education</topic><toplevel>online_resources</toplevel><creatorcontrib>Xu, E.</creatorcontrib><creatorcontrib>Liangshan Shao</creatorcontrib><creatorcontrib>Wendong Tan</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>Xu, E.</au><au>Liangshan Shao</au><au>Wendong Tan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Clustering Algorithm Based on Discretized Interval Value</atitle><btitle>The Proceedings of the Multiconference on "Computational Engineering in Systems Applications"</btitle><stitle>CESA</stitle><date>2006-10</date><risdate>2006</risdate><volume>1</volume><spage>864</spage><epage>868</epage><pages>864-868</pages><isbn>7302139229</isbn><isbn>9787302139225</isbn><eisbn>7900718141</eisbn><eisbn>9787900718143</eisbn><abstract>In order to improve the quality of traditional clustering algorithm and prevent the distribution of data from affecting the clustering algorithm greatly, a clustering algorithm based on interval value was proposed. Depending on the consistency of condition attributes and decision attributes in the decision table, the data was discretized and attributes were reduced by using data super-cube and information entropy. Based on the above, the algorithm can use the additivity of set feature vector to cluster data just by scanning the decision table only one time. Experimental results indicate that the algorithm is efficient and effective</abstract><pub>IEEE</pub><doi>10.1109/CESA.2006.4281773</doi><tpages>5</tpages></addata></record> |
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| subjects | Application software clustering Clustering algorithms Constraint theory decision table discretization Information entropy Information systems rough set set feature vector Set theory Space exploration Space technology Systems engineering and theory Systems engineering education |
| title | A Clustering Algorithm Based on Discretized Interval Value |
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