Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses.
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| Title: | Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses. |
|---|---|
| Authors: | Lee, Gangin1 ganginlee@sju.ac.kr, Yun, Unil1 unilyun@gmail.com, Lee, Kyung-Min2 kml@otsc.tamu.edu |
| Source: | Journal of Supercomputing. Nov2016, Vol. 72 Issue 11, p4296-4318. 23p. |
| Subjects: | Performance of supercomputers, Telnet (Computer network protocol), Intranets (Computer networks), Data mining, Performance evaluation |
| Abstract: | Various large-scale data have been generated in a variety of application fields, since the Internet began to be widely used. Accordingly, researchers have developed various data mining methods for pervasive human-centric computing to deal with the data and discover interesting knowledge. Frequent pattern mining is one of the main issues in data mining, which finds meaningful pattern information from databases. In this area, not only precise data but also uncertain data can be generated depending on environments of data generation. Since the concept of uncertain frequent pattern mining was proposed to overcome the limitations of traditional approaches that cannot deal with uncertain data with existential probabilities of items, several relevant methods have been developed. In this paper, we introduce and analyze state-of-the-art methods based on tree structures, and propose a new uncertain frequent pattern mining approach. We also compare algorithm performance and discuss characteristics of them. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 119139950 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Gangin%22">Lee, Gangin</searchLink><relatesTo>1</relatesTo><i> ganginlee@sju.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Yun%2C+Unil%22">Yun, Unil</searchLink><relatesTo>1</relatesTo><i> unilyun@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Kyung-Min%22">Lee, Kyung-Min</searchLink><relatesTo>2</relatesTo><i> kml@otsc.tamu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Nov2016, Vol. 72 Issue 11, p4296-4318. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Performance+of+supercomputers%22">Performance of supercomputers</searchLink><br /><searchLink fieldCode="DE" term="%22Telnet+%28Computer+network+protocol%29%22">Telnet (Computer network protocol)</searchLink><br /><searchLink fieldCode="DE" term="%22Intranets+%28Computer+networks%29%22">Intranets (Computer networks)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Various large-scale data have been generated in a variety of application fields, since the Internet began to be widely used. Accordingly, researchers have developed various data mining methods for pervasive human-centric computing to deal with the data and discover interesting knowledge. Frequent pattern mining is one of the main issues in data mining, which finds meaningful pattern information from databases. In this area, not only precise data but also uncertain data can be generated depending on environments of data generation. Since the concept of uncertain frequent pattern mining was proposed to overcome the limitations of traditional approaches that cannot deal with uncertain data with existential probabilities of items, several relevant methods have been developed. In this paper, we introduce and analyze state-of-the-art methods based on tree structures, and propose a new uncertain frequent pattern mining approach. We also compare algorithm performance and discuss characteristics of them. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11227-016-1847-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 4296 Subjects: – SubjectFull: Performance of supercomputers Type: general – SubjectFull: Telnet (Computer network protocol) Type: general – SubjectFull: Intranets (Computer networks) Type: general – SubjectFull: Data mining Type: general – SubjectFull: Performance evaluation Type: general Titles: – TitleFull: Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Gangin – PersonEntity: Name: NameFull: Yun, Unil – PersonEntity: Name: NameFull: Lee, Kyung-Min IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 72 – Type: issue Value: 11 Titles: – TitleFull: Journal of Supercomputing Type: main |
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