Interestingness measures for data mining: A survey
Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to...
Gespeichert in:
| Veröffentlicht in: | ACM computing surveys 2006-01, Vol.38 (3), p.9 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 3 |
| container_start_page | 9 |
| container_title | ACM computing surveys |
| container_volume | 38 |
| creator | Geng, Liqiang Hamilton, Howard J |
| description | Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area. |
| doi_str_mv | 10.1145/1132960.1132963 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_29842485</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1147086621</sourcerecordid><originalsourceid>FETCH-LOGICAL-c253t-1c417ef8bedc49e00059c8fe6563e8bdc652bb9c96f2e5d0b88e909337874eb3</originalsourceid><addsrcrecordid>eNpdkDFPwzAQhS0EEqUws0YMiCXU57Mde0QVhUqVWLpbiXNBqZqk2MnAv8dROzE93b1Pd0-PsUfgrwBSrQBQWD0Ps-IVW4BSRV6ghGu24Kh5zpHzW3YX44FzLiToBXvZ9iMFimPbf_cUY9ZRGae0yJohZHU5llnX9sm8ZzdNeYz0cNEl22_e9-vPfPf1sV2_7XIvFI45eAkFNaai2ktL6Y-y3jSklUYyVe21ElVlvdWNIFXzyhiy3CIWppBU4ZI9n8-ewvAzpVyua6On47HsaZiiE9ZIIY1K4NM_8DBMoU_RHFgFBpWABK3OkA9DjIEadwptV4ZfB9zNrblLaxdF_AM_rV2d</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>195183521</pqid></control><display><type>article</type><title>Interestingness measures for data mining: A survey</title><source>ACM Digital Library Complete</source><creator>Geng, Liqiang ; Hamilton, Howard J</creator><creatorcontrib>Geng, Liqiang ; Hamilton, Howard J</creatorcontrib><description>Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area.</description><identifier>ISSN: 0360-0300</identifier><identifier>EISSN: 1557-7341</identifier><identifier>DOI: 10.1145/1132960.1132963</identifier><language>eng</language><publisher>Baltimore: Association for Computing Machinery</publisher><subject>Algorithms ; Computer science ; Data mining ; Datasets ; Knowledge ; Mathematical models ; R&D ; Research & development ; Statistical analysis ; Studies</subject><ispartof>ACM computing surveys, 2006-01, Vol.38 (3), p.9</ispartof><rights>Copyright Association for Computing Machinery 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c253t-1c417ef8bedc49e00059c8fe6563e8bdc652bb9c96f2e5d0b88e909337874eb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,27911,27912</link.rule.ids></links><search><creatorcontrib>Geng, Liqiang</creatorcontrib><creatorcontrib>Hamilton, Howard J</creatorcontrib><title>Interestingness measures for data mining: A survey</title><title>ACM computing surveys</title><description>Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area.</description><subject>Algorithms</subject><subject>Computer science</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Knowledge</subject><subject>Mathematical models</subject><subject>R&D</subject><subject>Research & development</subject><subject>Statistical analysis</subject><subject>Studies</subject><issn>0360-0300</issn><issn>1557-7341</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNpdkDFPwzAQhS0EEqUws0YMiCXU57Mde0QVhUqVWLpbiXNBqZqk2MnAv8dROzE93b1Pd0-PsUfgrwBSrQBQWD0Ps-IVW4BSRV6ghGu24Kh5zpHzW3YX44FzLiToBXvZ9iMFimPbf_cUY9ZRGae0yJohZHU5llnX9sm8ZzdNeYz0cNEl22_e9-vPfPf1sV2_7XIvFI45eAkFNaai2ktL6Y-y3jSklUYyVe21ElVlvdWNIFXzyhiy3CIWppBU4ZI9n8-ewvAzpVyua6On47HsaZiiE9ZIIY1K4NM_8DBMoU_RHFgFBpWABK3OkA9DjIEadwptV4ZfB9zNrblLaxdF_AM_rV2d</recordid><startdate>20060101</startdate><enddate>20060101</enddate><creator>Geng, Liqiang</creator><creator>Hamilton, Howard J</creator><general>Association for Computing Machinery</general><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>20060101</creationdate><title>Interestingness measures for data mining</title><author>Geng, Liqiang ; Hamilton, Howard J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c253t-1c417ef8bedc49e00059c8fe6563e8bdc652bb9c96f2e5d0b88e909337874eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Computer science</topic><topic>Data mining</topic><topic>Datasets</topic><topic>Knowledge</topic><topic>Mathematical models</topic><topic>R&D</topic><topic>Research & development</topic><topic>Statistical analysis</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Geng, Liqiang</creatorcontrib><creatorcontrib>Hamilton, Howard J</creatorcontrib><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>ACM computing surveys</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Geng, Liqiang</au><au>Hamilton, Howard J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Interestingness measures for data mining: A survey</atitle><jtitle>ACM computing surveys</jtitle><date>2006-01-01</date><risdate>2006</risdate><volume>38</volume><issue>3</issue><spage>9</spage><pages>9-</pages><issn>0360-0300</issn><eissn>1557-7341</eissn><abstract>Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area.</abstract><cop>Baltimore</cop><pub>Association for Computing Machinery</pub><doi>10.1145/1132960.1132963</doi></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0360-0300 |
| ispartof | ACM computing surveys, 2006-01, Vol.38 (3), p.9 |
| issn | 0360-0300 1557-7341 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_29842485 |
| source | ACM Digital Library Complete |
| subjects | Algorithms Computer science Data mining Datasets Knowledge Mathematical models R&D Research & development Statistical analysis Studies |
| title | Interestingness measures for data mining: A survey |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T15%3A30%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Interestingness%20measures%20for%20data%20mining:%20A%20survey&rft.jtitle=ACM%20computing%20surveys&rft.au=Geng,%20Liqiang&rft.date=2006-01-01&rft.volume=38&rft.issue=3&rft.spage=9&rft.pages=9-&rft.issn=0360-0300&rft.eissn=1557-7341&rft_id=info:doi/10.1145/1132960.1132963&rft_dat=%3Cproquest_cross%3E1147086621%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=195183521&rft_id=info:pmid/&rfr_iscdi=true |