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http://hdl.handle.net/1942/6669
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bonchi, F. | - |
dc.contributor.author | GOETHALS, Bart | - |
dc.date.accessioned | 2007-12-20T16:09:38Z | - |
dc.date.available | 2007-12-20T16:09:38Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | Advances in Knowledge Discovery and Data Mining. p. 155-160 | - |
dc.identifier.isbn | 978-3-540-22064-0 | - |
dc.identifier.uri | http://hdl.handle.net/1942/6669 | - |
dc.description.abstract | In the context of mining frequent itemsets, numerous strategies have been proposed to push several types of constraints within the most well known algorithms. In this paper, we integrate the recently proposed ExAnte data reduction technique within the FP-growth algorithm. Together, they result in a very efficient frequent itemset mining algorithm that effectively exploits monotone constraints. | - |
dc.language.iso | en | - |
dc.publisher | Berlin Springer | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science | - |
dc.title | FP-bonsai: the art of growing and pruning small FP-trees | - |
dc.title.alternative | 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004. Proceedings | - |
dc.type | Journal Contribution | - |
local.bibliographicCitation.conferencename | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | - |
dc.identifier.epage | 160 | - |
dc.identifier.spage | 155 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.relation.ispartofseriesnr | 3056 | - |
dc.bibliographicCitation.oldjcat | A1 | - |
local.class | dsPublValOverrule/no_publishing_delay | - |
dc.identifier.doi | 10.1007/b97861 | - |
dc.identifier.isi | 000221955100017 | - |
item.fulltext | No Fulltext | - |
item.contributor | Bonchi, F. | - |
item.contributor | GOETHALS, Bart | - |
item.fullcitation | Bonchi, F. & GOETHALS, Bart (2004) FP-bonsai: the art of growing and pruning small FP-trees. In: Advances in Knowledge Discovery and Data Mining. p. 155-160. | - |
item.accessRights | Closed Access | - |
Appears in Collections: | Research publications |
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