MVS-based semi-supervised clustering
Semi-supervised clustering is a popular machine learning technique, used for challenge data categorization tasks, when some prior knowledge is available to users. In this paper, we report the empirical studies on our newly proposed semi-supervised clustering framework, which utilizes multiple viewpo...
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| creator | Yang Yan Lihui Chen Chee Keong Chan |
| description | Semi-supervised clustering is a popular machine learning technique, used for challenge data categorization tasks, when some prior knowledge is available to users. In this paper, we report the empirical studies on our newly proposed semi-supervised clustering framework, which utilizes multiple viewpoints for the similarity measure, with the help of the prior knowledge. Two different MVS-based approaches are developed for knowledge given in either class labels or pair-wise constraints, namely LMVS and PMVS respectively. Extensive experimental studies performed on a few benchmark datasets demonstrate the effectiveness of the proposed methods. Comparisons are also made between LMVS and PMVS, together with a few well-known semi-supervised clustering algorithms. |
| doi_str_mv | 10.1109/ICICS.2013.6782907 |
| format | Conference Proceeding |
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Comparisons are also made between LMVS and PMVS, together with a few well-known semi-supervised clustering algorithms.</description><subject>Accuracy</subject><subject>Benchmark testing</subject><subject>class labels</subject><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>Educational institutions</subject><subject>Measurement</subject><subject>multi-viewpoint based similarity</subject><subject>pair-wise constraint</subject><subject>semi-supervised clustering</subject><subject>Vectors</subject><isbn>1479904333</isbn><isbn>1479904341</isbn><isbn>9781479904341</isbn><isbn>9781479904334</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj01LAzEURdOF0Fr7B3Tjwm3G9_ImX0sZ1A5UXFS7LUn6IpFWyqQV_PdW7Orew4UDV4hrhAYR_H3f9d2yUYDUGOuUBzsSl9ha76ElorGY1foJAGitQgUTcfeyWsoYKm9uK--KrMc9D9_lj9P2WA88lK-PK3GRw7by7JxT8f70-NbN5eL1ue8eFrKg1QcZvc8-JkU6WE7OpETkWQO405BwY41jNjEgBKdzOJUQss4pJxNta5Cm4ubfW5h5vR_KLgw_6_MR-gWclz6w</recordid><startdate>201312</startdate><enddate>201312</enddate><creator>Yang Yan</creator><creator>Lihui Chen</creator><creator>Chee Keong Chan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201312</creationdate><title>MVS-based semi-supervised clustering</title><author>Yang Yan ; Lihui Chen ; Chee Keong Chan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b99f9bc235a7ec86cc339e500899fc1d768ee6ba10a85faba1aaf5fcfc6b74613</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accuracy</topic><topic>Benchmark testing</topic><topic>class labels</topic><topic>Clustering algorithms</topic><topic>Clustering methods</topic><topic>Educational institutions</topic><topic>Measurement</topic><topic>multi-viewpoint based similarity</topic><topic>pair-wise constraint</topic><topic>semi-supervised clustering</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Yang Yan</creatorcontrib><creatorcontrib>Lihui Chen</creatorcontrib><creatorcontrib>Chee Keong Chan</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>Yang Yan</au><au>Lihui Chen</au><au>Chee Keong Chan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>MVS-based semi-supervised clustering</atitle><btitle>2013 9th International Conference on Information, Communications & Signal Processing</btitle><stitle>ICICS</stitle><date>2013-12</date><risdate>2013</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eisbn>1479904333</eisbn><eisbn>1479904341</eisbn><eisbn>9781479904341</eisbn><eisbn>9781479904334</eisbn><abstract>Semi-supervised clustering is a popular machine learning technique, used for challenge data categorization tasks, when some prior knowledge is available to users. 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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Benchmark testing class labels Clustering algorithms Clustering methods Educational institutions Measurement multi-viewpoint based similarity pair-wise constraint semi-supervised clustering Vectors |
| title | MVS-based semi-supervised clustering |
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