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GRG: knowledge discovery using information generalization, information reduction, and rule generation
We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascension technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of...
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| creator | Ning Shan Hamilton, H.J. Cercone, N. |
| description | We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascension technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, the reduction technique is applied to generate a minimized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database. |
| doi_str_mv | 10.1109/TAI.1995.479781 |
| format | conference_proceeding |
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In the first step, an attribute-oriented concept tree ascension technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, the reduction technique is applied to generate a minimized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database.</description><identifier>ISSN: 1082-3409</identifier><identifier>ISBN: 0818673125</identifier><identifier>ISBN: 9780818673122</identifier><identifier>EISSN: 2375-0197</identifier><identifier>DOI: 10.1109/TAI.1995.479781</identifier><language>eng</language><publisher>IEEE Comput. Soc. Technical Committee on Pattern Analysis and Machine Intelligence</publisher><subject>Computational complexity ; Computer science ; Data analysis ; Data mining ; Decision support systems ; Information systems ; Knowledge representation ; Machine learning ; Relational databases ; Rough sets</subject><ispartof>Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, 1995, p.372-379</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/479781$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27901,54529,54894,54906</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/479781$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ning Shan</creatorcontrib><creatorcontrib>Hamilton, H.J.</creatorcontrib><creatorcontrib>Cercone, N.</creatorcontrib><title>GRG: knowledge discovery using information generalization, information reduction, and rule generation</title><title>Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence</title><addtitle>TAI</addtitle><description>We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascension technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, the reduction technique is applied to generate a minimized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database.</description><subject>Computational complexity</subject><subject>Computer science</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Decision support systems</subject><subject>Information systems</subject><subject>Knowledge representation</subject><subject>Machine learning</subject><subject>Relational databases</subject><subject>Rough sets</subject><issn>1082-3409</issn><issn>2375-0197</issn><isbn>0818673125</isbn><isbn>9780818673122</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1995</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVUMtKAzEUDT7AaXUtuJoPcOq9eTQTd6XUWigIUtclTW6G6HRGMh2lfr2PduPqcB4cOIexa4QRIpi71WQxQmPUSGqjSzxhGRdaFYBGn7IBlFiOtUCuzliGUPJCSDAXbNB1rwAcFBcZo_nz_D5_a9rPmnxFuY-daz8o7fO-i02Vxya0aWt3sW3yihpKto5ff_T2n5fI9-4g28bnqa_pmP8VL9l5sHVHV0ccspeH2Wr6WCyf5ovpZFlEBLkrUNhxQJRBamHJh-AkBC-xRGfdxngnAvd2oxUEzSkIBfSzCxU31sFYODFkN4feSETr9xS3Nu3Xh3fEN9Q5WUM</recordid><startdate>1995</startdate><enddate>1995</enddate><creator>Ning Shan</creator><creator>Hamilton, H.J.</creator><creator>Cercone, N.</creator><general>IEEE Comput. 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Technical Committee on Pattern Analysis and Machine Intelligence</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1995</creationdate><title>GRG: knowledge discovery using information generalization, information reduction, and rule generation</title><author>Ning Shan ; Hamilton, H.J. ; Cercone, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-13a6f114f473aedffc40fd4181cacb9dc3f2dab750f72ef350e1081529ac063c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Computational complexity</topic><topic>Computer science</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Decision support systems</topic><topic>Information systems</topic><topic>Knowledge representation</topic><topic>Machine learning</topic><topic>Relational databases</topic><topic>Rough sets</topic><toplevel>online_resources</toplevel><creatorcontrib>Ning Shan</creatorcontrib><creatorcontrib>Hamilton, H.J.</creatorcontrib><creatorcontrib>Cercone, 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 Online</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>Ning Shan</au><au>Hamilton, H.J.</au><au>Cercone, N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>GRG: knowledge discovery using information generalization, information reduction, and rule generation</atitle><btitle>Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence</btitle><stitle>TAI</stitle><date>1995</date><risdate>1995</risdate><spage>372</spage><epage>379</epage><pages>372-379</pages><issn>1082-3409</issn><eissn>2375-0197</eissn><isbn>0818673125</isbn><isbn>9780818673122</isbn><abstract>We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascension technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, the reduction technique is applied to generate a minimized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database.</abstract><pub>IEEE Comput. Soc. Technical Committee on Pattern Analysis and Machine Intelligence</pub><doi>10.1109/TAI.1995.479781</doi><tpages>8</tpages></addata></record> |
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| identifier | ISSN: 1082-3409 |
| ispartof | Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, 1995, p.372-379 |
| issn | 1082-3409 2375-0197 |
| language | eng |
| recordid | cdi_ieee_primary_479781 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computational complexity Computer science Data analysis Data mining Decision support systems Information systems Knowledge representation Machine learning Relational databases Rough sets |
| title | GRG: knowledge discovery using information generalization, information reduction, and rule generation |
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