File Download
There are no files associated with this item.
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Mining frequent itemsets from uncertain data
Title | Mining frequent itemsets from uncertain data |
---|---|
Authors | |
Issue Date | 2007 |
Publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ |
Citation | Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4426 LNAI, p. 47-58 How to Cite? |
Abstract | We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost. © Springer-Verlag Berlin Heidelberg 2007. |
Persistent Identifier | http://hdl.handle.net/10722/93484 |
ISSN | 2020 SCImago Journal Rankings: 0.249 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chui, CK | en_HK |
dc.contributor.author | Kao, B | en_HK |
dc.contributor.author | Hung, E | en_HK |
dc.date.accessioned | 2010-09-25T15:02:33Z | - |
dc.date.available | 2010-09-25T15:02:33Z | - |
dc.date.issued | 2007 | en_HK |
dc.identifier.citation | Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4426 LNAI, p. 47-58 | en_HK |
dc.identifier.issn | 0302-9743 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/93484 | - |
dc.description.abstract | We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost. © Springer-Verlag Berlin Heidelberg 2007. | en_HK |
dc.language | eng | en_HK |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | en_HK |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_HK |
dc.title | Mining frequent itemsets from uncertain data | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Kao, B:kao@cs.hku.hk | en_HK |
dc.identifier.authority | Kao, B=rp00123 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-38049177468 | en_HK |
dc.identifier.hkuros | 137096 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-38049177468&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 4426 LNAI | en_HK |
dc.identifier.spage | 47 | en_HK |
dc.identifier.epage | 58 | en_HK |
dc.publisher.place | Germany | en_HK |
dc.identifier.scopusauthorid | Chui, CK=21741958100 | en_HK |
dc.identifier.scopusauthorid | Kao, B=35221592600 | en_HK |
dc.identifier.scopusauthorid | Hung, E=7004256336 | en_HK |
dc.identifier.issnl | 0302-9743 | - |