A fast algorithm to cluster high dimensional basket data
Clustering is a data mining problem that has received significant attention by the database community. Data set size, dimensionality and sparsity have been identified as aspects that make clustering more difficult. The article introduces a fast algorithm to cluster large binary data sets where data...
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| creator | Ordonez, C. Omiecinski, E. Ezquerra, N. |
| description | Clustering is a data mining problem that has received significant attention by the database community. Data set size, dimensionality and sparsity have been identified as aspects that make clustering more difficult. The article introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero. This is the case with basket data transactions containing items, that can be represented as sparse binary vectors with very high dimensionality. An experimental section shows performance, advantages and limitations of the proposed approach. |
| doi_str_mv | 10.1109/ICDM.2001.989586 |
| format | Conference Proceeding |
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Data set size, dimensionality and sparsity have been identified as aspects that make clustering more difficult. The article introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero. This is the case with basket data transactions containing items, that can be represented as sparse binary vectors with very high dimensionality. An experimental section shows performance, advantages and limitations of the proposed approach.</description><identifier>ISBN: 9780769511191</identifier><identifier>ISBN: 0769511198</identifier><identifier>DOI: 10.1109/ICDM.2001.989586</identifier><language>eng</language><publisher>IEEE</publisher><subject>Association rules ; Clustering algorithms ; Data mining ; Educational institutions ; Maximum likelihood estimation ; Multidimensional systems ; Partitioning algorithms ; Sparse matrices ; Statistical analysis</subject><ispartof>Proceedings 2001 IEEE International Conference on Data Mining, 2001, p.633-636</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/989586$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/989586$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ordonez, C.</creatorcontrib><creatorcontrib>Omiecinski, E.</creatorcontrib><creatorcontrib>Ezquerra, N.</creatorcontrib><title>A fast algorithm to cluster high dimensional basket data</title><title>Proceedings 2001 IEEE International Conference on Data Mining</title><addtitle>ICDM</addtitle><description>Clustering is a data mining problem that has received significant attention by the database community. Data set size, dimensionality and sparsity have been identified as aspects that make clustering more difficult. The article introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero. This is the case with basket data transactions containing items, that can be represented as sparse binary vectors with very high dimensionality. An experimental section shows performance, advantages and limitations of the proposed approach.</description><subject>Association rules</subject><subject>Clustering algorithms</subject><subject>Data mining</subject><subject>Educational institutions</subject><subject>Maximum likelihood estimation</subject><subject>Multidimensional systems</subject><subject>Partitioning algorithms</subject><subject>Sparse matrices</subject><subject>Statistical analysis</subject><isbn>9780769511191</isbn><isbn>0769511198</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2001</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tOwzAQAC0hJFDJHXHyD6TsxnW8PlbhVakVFzhXWz8aQ9Kg2Bz4e5DKXOY20ghxi7BEBHu_6R52ywYAl5aspvZCVNYQmNZqRLR4JaqcP-CPlUZozbWgtYyci-ThOM2p9KMsk3TDdy5hln069tKnMZxymk48yAPnz1Ck58I34jLykEP174V4f3p8617q7evzpltv64SmKbUOQNFbokZBJFbEJpIHhpZj1NFHR8DeNwdsnQ7G4MqBa0hZco6Vsmoh7s7dFELYf81p5Plnf95Tv2jIRU8</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Ordonez, C.</creator><creator>Omiecinski, E.</creator><creator>Ezquerra, N.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2001</creationdate><title>A fast algorithm to cluster high dimensional basket data</title><author>Ordonez, C. ; Omiecinski, E. ; Ezquerra, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-5e08fd988230f8a38a7f8d0a06aff5fdfc80add2b16c5e7714c0c28398cca3393</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Association rules</topic><topic>Clustering algorithms</topic><topic>Data mining</topic><topic>Educational institutions</topic><topic>Maximum likelihood estimation</topic><topic>Multidimensional systems</topic><topic>Partitioning algorithms</topic><topic>Sparse matrices</topic><topic>Statistical analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Ordonez, C.</creatorcontrib><creatorcontrib>Omiecinski, E.</creatorcontrib><creatorcontrib>Ezquerra, N.</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>Ordonez, C.</au><au>Omiecinski, E.</au><au>Ezquerra, N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A fast algorithm to cluster high dimensional basket data</atitle><btitle>Proceedings 2001 IEEE International Conference on Data Mining</btitle><stitle>ICDM</stitle><date>2001</date><risdate>2001</risdate><spage>633</spage><epage>636</epage><pages>633-636</pages><isbn>9780769511191</isbn><isbn>0769511198</isbn><abstract>Clustering is a data mining problem that has received significant attention by the database community. Data set size, dimensionality and sparsity have been identified as aspects that make clustering more difficult. The article introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero. This is the case with basket data transactions containing items, that can be represented as sparse binary vectors with very high dimensionality. An experimental section shows performance, advantages and limitations of the proposed approach.</abstract><pub>IEEE</pub><doi>10.1109/ICDM.2001.989586</doi><tpages>4</tpages></addata></record> |
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| ispartof | Proceedings 2001 IEEE International Conference on Data Mining, 2001, p.633-636 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Association rules Clustering algorithms Data mining Educational institutions Maximum likelihood estimation Multidimensional systems Partitioning algorithms Sparse matrices Statistical analysis |
| title | A fast algorithm to cluster high dimensional basket data |
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