Location- and density-based hierarchical clustering using similarity analysis
This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations o...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 1998-09, Vol.20 (9), p.1011-1015 |
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| container_issue | 9 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 20 |
| creator | Bajcsy, P. Ahuja, N. |
| description | This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation. |
| doi_str_mv | 10.1109/34.713365 |
| format | Article |
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Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation.</description><subject>Character recognition</subject><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>Graph theory</subject><subject>Image analysis</subject><subject>Image color analysis</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Image texture analysis</subject><subject>Pattern analysis</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0D1Pw0AMBuATAolSGFiZMiExpPjOuSQ3ooovKYgF5uhyceihNCnnZOi_J1UqVhZblh97eIW4lrCSEsw9JqtMIqb6RCykQROjRnMqFiBTFee5ys_FBfM3gEw04EK8Fb2zg--7OLJdHdXUsR_2cWWZ6mjjKdjgNt7ZNnLtyAMF331FIx8q-61vbZj4dGrbPXu-FGeNbZmujn0pPp8eP9YvcfH-_Lp-KGKHkA9xWqOuKpWghqSpZE5oG6cMAKUpkK1UZsBacroGJatGZa5plKrBVFhPc4JLcTv_3YX-ZyQeyq1nR21rO-pHLlWOWurU_A8znWWJPsC7GbrQMwdqyl3wWxv2pYTykGyJSTknO9mb2Xoi-nPH5S-_4HRh</recordid><startdate>19980901</startdate><enddate>19980901</enddate><creator>Bajcsy, P.</creator><creator>Ahuja, N.</creator><general>IEEE</general><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19980901</creationdate><title>Location- and density-based hierarchical clustering using similarity analysis</title><author>Bajcsy, P. ; Ahuja, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c308t-6d35bb243504fb18e3afc2900e660eab2790aaec5d021bf27cff22d09b3d1bf43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Character recognition</topic><topic>Clustering algorithms</topic><topic>Clustering methods</topic><topic>Graph theory</topic><topic>Image analysis</topic><topic>Image color analysis</topic><topic>Image edge detection</topic><topic>Image segmentation</topic><topic>Image texture analysis</topic><topic>Pattern analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bajcsy, P.</creatorcontrib><creatorcontrib>Ahuja, N.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bajcsy, P.</au><au>Ahuja, N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Location- and density-based hierarchical clustering using similarity analysis</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><date>1998-09-01</date><risdate>1998</risdate><volume>20</volume><issue>9</issue><spage>1011</spage><epage>1015</epage><pages>1011-1015</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>This paper presents a new approach to hierarchical clustering of point patterns. 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| ispartof | IEEE transactions on pattern analysis and machine intelligence, 1998-09, Vol.20 (9), p.1011-1015 |
| issn | 0162-8828 1939-3539 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Character recognition Clustering algorithms Clustering methods Graph theory Image analysis Image color analysis Image edge detection Image segmentation Image texture analysis Pattern analysis |
| title | Location- and density-based hierarchical clustering using similarity analysis |
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