A comparison of some clustering techniques via color segmentation
This paper proposes a new improved modified mountain clustering technique. The proposed technique is being compared with some existing techniques such as FCM, Gath-Geva, probabilistic clustering and modified mountain clustering. The performance of all these clustering techniques is compared by apply...
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| creator | Agarwal, S. Madasu, S. Hanmandlu, M. Vasikarla, S. |
| description | This paper proposes a new improved modified mountain clustering technique. The proposed technique is being compared with some existing techniques such as FCM, Gath-Geva, probabilistic clustering and modified mountain clustering. The performance of all these clustering techniques is compared by applying them to color segmentation in terms of cluster validity and computational complexity. |
| doi_str_mv | 10.1109/ITCC.2005.4 |
| format | Conference Proceeding |
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The proposed technique is being compared with some existing techniques such as FCM, Gath-Geva, probabilistic clustering and modified mountain clustering. The performance of all these clustering techniques is compared by applying them to color segmentation in terms of cluster validity and computational complexity.</description><identifier>ISBN: 0769523153</identifier><identifier>ISBN: 9780769523156</identifier><identifier>DOI: 10.1109/ITCC.2005.4</identifier><language>eng</language><publisher>IEEE</publisher><subject>Clustering algorithms ; Clustering methods ; Color segmentation ; Computational complexity ; Data mining ; EM algorithm and cluster validity ; Fuzzy set theory ; Gath-Geva and Fuzzy C-Means clustering techniques ; Image retrieval ; Image segmentation ; Information retrieval ; Modified mountain ; Pattern classification ; Probabilistic ; Set theory</subject><ispartof>International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II, 2005, Vol.2, p.147-153 Vol. 2</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/1425137$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2057,4049,4050,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1425137$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Agarwal, S.</creatorcontrib><creatorcontrib>Madasu, S.</creatorcontrib><creatorcontrib>Hanmandlu, M.</creatorcontrib><creatorcontrib>Vasikarla, S.</creatorcontrib><title>A comparison of some clustering techniques via color segmentation</title><title>International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II</title><addtitle>ITCC</addtitle><description>This paper proposes a new improved modified mountain clustering technique. The proposed technique is being compared with some existing techniques such as FCM, Gath-Geva, probabilistic clustering and modified mountain clustering. The performance of all these clustering techniques is compared by applying them to color segmentation in terms of cluster validity and computational complexity.</description><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>Color segmentation</subject><subject>Computational complexity</subject><subject>Data mining</subject><subject>EM algorithm and cluster validity</subject><subject>Fuzzy set theory</subject><subject>Gath-Geva and Fuzzy C-Means clustering techniques</subject><subject>Image retrieval</subject><subject>Image segmentation</subject><subject>Information retrieval</subject><subject>Modified mountain</subject><subject>Pattern classification</subject><subject>Probabilistic</subject><subject>Set theory</subject><isbn>0769523153</isbn><isbn>9780769523156</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjLtOwzAUQC0hJKB0YmTxDyTYvn6OUcSjUqUuZa4c57oYJXGJUyT-nkpwlrMcHUIeOKs5Z-5ps2_bWjCmanlF7pjRTgngCm7IupRPdgGcdJrdkqahIY8nP6eSJ5ojLXlEGoZzWXBO05EuGD6m9HXGQr-Tv8RDnmnB44jT4peUp3tyHf1QcP3vFXl_ed63b9V297ppm22VuFFLFbTVDrTuFe-k5X20IQbWGS9M7CMHBSC50WCtCl50oo9So9DAmUUdHIMVefz7JkQ8nOY0-vnnwKVQHAz8Ah45Rqk</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Agarwal, S.</creator><creator>Madasu, S.</creator><creator>Hanmandlu, M.</creator><creator>Vasikarla, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2005</creationdate><title>A comparison of some clustering techniques via color segmentation</title><author>Agarwal, S. ; Madasu, S. ; Hanmandlu, M. ; Vasikarla, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c6869366d51b481df8cfc0b7a27fdf1353341763885ca2b2df46e263108e6c903</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Clustering algorithms</topic><topic>Clustering methods</topic><topic>Color segmentation</topic><topic>Computational complexity</topic><topic>Data mining</topic><topic>EM algorithm and cluster validity</topic><topic>Fuzzy set theory</topic><topic>Gath-Geva and Fuzzy C-Means clustering techniques</topic><topic>Image retrieval</topic><topic>Image segmentation</topic><topic>Information retrieval</topic><topic>Modified mountain</topic><topic>Pattern classification</topic><topic>Probabilistic</topic><topic>Set theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Agarwal, S.</creatorcontrib><creatorcontrib>Madasu, S.</creatorcontrib><creatorcontrib>Hanmandlu, M.</creatorcontrib><creatorcontrib>Vasikarla, S.</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>Agarwal, S.</au><au>Madasu, S.</au><au>Hanmandlu, M.</au><au>Vasikarla, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A comparison of some clustering techniques via color segmentation</atitle><btitle>International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II</btitle><stitle>ITCC</stitle><date>2005</date><risdate>2005</risdate><volume>2</volume><spage>147</spage><epage>153 Vol. 2</epage><pages>147-153 Vol. 2</pages><isbn>0769523153</isbn><isbn>9780769523156</isbn><abstract>This paper proposes a new improved modified mountain clustering technique. The proposed technique is being compared with some existing techniques such as FCM, Gath-Geva, probabilistic clustering and modified mountain clustering. The performance of all these clustering techniques is compared by applying them to color segmentation in terms of cluster validity and computational complexity.</abstract><pub>IEEE</pub><doi>10.1109/ITCC.2005.4</doi></addata></record> |
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| ispartof | International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II, 2005, Vol.2, p.147-153 Vol. 2 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Clustering algorithms Clustering methods Color segmentation Computational complexity Data mining EM algorithm and cluster validity Fuzzy set theory Gath-Geva and Fuzzy C-Means clustering techniques Image retrieval Image segmentation Information retrieval Modified mountain Pattern classification Probabilistic Set theory |
| title | A comparison of some clustering techniques via color segmentation |
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