{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T02:01:31Z","timestamp":1791079291273,"version":"4.1.0"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Database Syst."],"published-print":{"date-parts":[[2006,12]]},"abstract":"<jats:p>\n            The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is: what's an appropriate foundation for data mining, which can accommodate disparate mining tasks? We address this problem by presenting a database model and an algebra for data mining. The database model is based on the 3W-model introduced by Johnson et al. [2000]. This model relied on black box mining operators. A main contribution of this article is to open up these black boxes, by using generic operators in a data mining algebra. Two key operators in this algebra are\n            <jats:italic>regionize<\/jats:italic>\n            , which creates regions (or models) from data tuples, and a restricted form of looping called\n            <jats:italic>mining loop<\/jats:italic>\n            . Then the resulting data mining algebra MA is studied and properties concerning expressive power and complexity are established. We present results in three directions: (1) expressiveness of the mining algebra; (2) relations with alternative frameworks, and (3) interactions between regionize and mining loop.\n          <\/jats:p>","DOI":"10.1145\/1189769.1189770","type":"journal-article","created":{"date-parts":[[2007,1,16]],"date-time":"2007-01-16T19:38:29Z","timestamp":1168976309000},"page":"1169-1214","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Expressive power of an algebra for data mining"],"prefix":"10.1145","volume":"31","author":[{"given":"Toon","family":"Calders","sequence":"first","affiliation":[{"name":"Eindhoven Technical University, Eindhoven, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laks V. 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In Proceedings of the ICDT International Conference on Database Theory, G. Gottlob and M. Vardi, Eds. Lecture Notes in Computer Science , vol. 893 . Springer-Verlag, London, U.K., 439--454.]] Abiteboul, S. and Hillebrand, G. G. 1995. Space usage in functional query languages. In Proceedings of the ICDT International Conference on Database Theory, G. Gottlob and M. Vardi, Eds. Lecture Notes in Computer Science, vol. 893. Springer-Verlag, London, U.K., 439--454.]]"},{"key":"e_1_2_2_2_1","unstructured":"Abiteboul S. Hull R. and Vianu V. 1995. Foundations of Databases. Addison-Wesley Reading MA.]]   Abiteboul S. Hull R. and Vianu V. 1995. Foundations of Databases. Addison-Wesley Reading MA.]]"},{"key":"e_1_2_2_3_1","volume-title":"Proceedings of the VLDB International Conference on Very Large Data Bases. Morgan Kaufmann","author":"Agrawal A.","unstructured":"Agrawal , A. and Srikant , R . 1994. Fast algorithms for mining association rules . 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