Scalable Multi-Relational Association Mining

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dc.contributor.author Clare, Amanda
dc.contributor.author Williams, Hugh E.
dc.contributor.author Lester, Nicholas
dc.date.accessioned 2006-04-24T15:16:38Z
dc.date.available 2006-04-24T15:16:38Z
dc.date.issued 2004
dc.identifier.citation Clare , A , Williams , H E & Lester , N 2004 , ' Scalable Multi-Relational Association Mining ' . en
dc.identifier.other PURE: 68027
dc.identifier.other dspace: 2160/127
dc.identifier.uri http://hdl.handle.net/2160/127
dc.description Clare, A., Williams, H. E. and Lester, N. M. (2004) Scalable Multi-Relational Association Mining. In proceedings of the 4th International Conference on Data Mining ICDM '04. en
dc.description.abstract We propose the new RADAR technique for multi-relational data mining. This permits the mining of very large collections and provides a new technique for discovering multi-relational associations. Results show that RADAR is reliable and scalable for mining a large yeast homology collection, and that it does not have the main-memory scalability constraints of the Farmer and Warmr tools. en
dc.language.iso eng
dc.title Scalable Multi-Relational Association Mining en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Computational Biology and Bioinformatics en
dc.description.status Non peer reviewed en


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