Mining multiple-level association rules in large databases
A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the a priori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance t...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 1999-09, Vol.11 (5), p.798-805 |
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| container_title | IEEE transactions on knowledge and data engineering |
| container_volume | 11 |
| creator | Han, Jiawei Fu, Yongjian |
| description | A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the a priori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding "level-crossing" association rules, are also investigated. The study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules. |
| doi_str_mv | 10.1109/69.806937 |
| format | Article |
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The study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Association rules</subject><subject>Computer Society</subject><subject>Concrete</subject><subject>Dairy products</subject><subject>Data mining</subject><subject>Mining</subject><subject>Performance analysis</subject><subject>Taxonomy</subject><subject>Testing</subject><subject>Transaction databases</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0U1Lw0AQBuBFFKzVg1dPOSkeUmd2N_vhTYpfUPGi57DdTMrKNqnZVPDfG0nxaE8zMA_DMC9j5wgzRLA3ys4MKCv0AZtgUZico8XDoQeJuRRSH7OTlD4AwGiDE3b7EprQrLL1NvZhEymP9EUxcym1Prg-tE3WbSOlLDRZdN2Kssr1bukSpVN2VLuY6GxXp-z94f5t_pQvXh-f53eL3AuNfa5r1EWFwCV6L5baQ8FdLYHzpRpmUGvjtVXeOeslR21JcM2x0sqYWtWVmLKrce-maz-3lPpyHZKnGF1D7TaVFq1FLkEP8vJfya1QghfFfmiG0yVX-6EGGB5sB3g9Qt-1KXVUl5surF33XSKUv8mUypZjMoO9GG0goj-3G_4AqsKGnQ</recordid><startdate>19990901</startdate><enddate>19990901</enddate><creator>Han, Jiawei</creator><creator>Fu, Yongjian</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><scope>7TB</scope><scope>FR3</scope><scope>7SP</scope><scope>F28</scope></search><sort><creationdate>19990901</creationdate><title>Mining multiple-level association rules in large databases</title><author>Han, Jiawei ; Fu, Yongjian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-7f175d10241cc3b7c052af4022b67f10f78c796caa9c42179e32721d7688f6fd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Algorithm design and analysis</topic><topic>Algorithms</topic><topic>Association rules</topic><topic>Computer Society</topic><topic>Concrete</topic><topic>Dairy products</topic><topic>Data mining</topic><topic>Mining</topic><topic>Performance analysis</topic><topic>Taxonomy</topic><topic>Testing</topic><topic>Transaction databases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Han, Jiawei</creatorcontrib><creatorcontrib>Fu, Yongjian</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><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><collection>Electronics & Communications Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on knowledge and data engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Han, Jiawei</au><au>Fu, Yongjian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mining multiple-level association rules in large databases</atitle><jtitle>IEEE transactions on knowledge and data engineering</jtitle><stitle>TKDE</stitle><date>1999-09-01</date><risdate>1999</risdate><volume>11</volume><issue>5</issue><spage>798</spage><epage>805</epage><pages>798-805</pages><issn>1041-4347</issn><eissn>1558-2191</eissn><coden>ITKEEH</coden><abstract>A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the a priori principle. 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| ispartof | IEEE transactions on knowledge and data engineering, 1999-09, Vol.11 (5), p.798-805 |
| issn | 1041-4347 1558-2191 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_919912407 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Association rules Computer Society Concrete Dairy products Data mining Mining Performance analysis Taxonomy Testing Transaction databases |
| title | Mining multiple-level association rules in large databases |
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