Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/78230
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dc.titleMining frequent itemsets using support constraints
dc.contributor.authorWang, K.
dc.contributor.authorHe, Y.
dc.contributor.authorHan, J.
dc.date.accessioned2014-07-04T03:13:54Z
dc.date.available2014-07-04T03:13:54Z
dc.date.issued2000
dc.identifier.citationWang, K.,He, Y.,Han, J. (2000). Mining frequent itemsets using support constraints. Proceedings of the 26th International Conference on Very Large Data Bases, VLDB'00 : 43-52. ScholarBank@NUS Repository.
dc.identifier.isbn1558607153
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/78230
dc.description.abstractInteresting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Aprio.-i, either misses interesting patterns of low support or suffers from the bottleneck of itemset generation. A better solution is to exploit Support constraints, which specify what minimum support is required for what itemseta, so that only necessary itemsets are generated. In this paper, we present a framework of frequent itemset mining in the presence of support constraints. Our approach is to "push" support constraints into the Apriori it.emset generation so that the "best" minimum support is used for each itemset at run time to preserve the essence of Apriori.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings of the 26th International Conference on Very Large Data Bases, VLDB'00
dc.description.page43-52
dc.identifier.isiutNOT_IN_WOS
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