SPPC: a new tree structure for mining erasable patterns in data streams.
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| Title: | SPPC: a new tree structure for mining erasable patterns in data streams. |
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| Authors: | Le, Tuong1 tuonglc@sju.ac.kr, Vo, Bay2,3 vodinhbay@tdtu.edu.vn, Fournier-Viger, Philippe4 philfv@hit.edu.cn, Lee, Mi Young1 miylee@sejong.ac.kr, Baik, Sung Wook1 sbaik@sejong.ac.kr |
| Source: | Applied Intelligence. Feb2019, Vol. 49 Issue 2, p478-495. 18p. |
| Subjects: | Erasable programmable read-only memory, Data mining, Decision making, Planography, Meta-analysis |
| Abstract: | Discovering Erasable Patterns (EPs) consists of identifying product parts that will produce a small profit loss if their production is stopped. It is a data mining problem that has attracted the attention of numerous researchers in recent years due to the possibility of using EPs to reduce profit loss of manufacturers. Though, many algorithms have been designed to mine EPs, an important limitation of state-of-the-art EP mining algorithms is that they are batch algorithms, that is, they are designed to be applied on static databases. But in real-life applications, databases are dynamic, as they are constantly updated by adding or removing products and parts. To be informed about EPs in real-time, traditional EP mining algorithms must be applied over and over again on a database. This is inefficient as those algorithms are always applied from scratch without taking advantage of results generated by previous executions. Considering this important drawback of previous work for handling real-life dynamic data, this paper proposes an efficient algorithm named MSPPC for mining EPs in data streams. It relies on a novel tree structure named SPPC (Streaming Pre-Post Code) tree, which extends the WPPC tree structure for maintaining a compact tree representation of EPs in a data stream. Experimental results show that the designed MSPPC algorithm outperforms the state-of-the-art batch MERIT and dMERIT algorithms when they are run in batch mode using a sliding-window. Besides, the proposed algorithm is also faster than the state-of-the-art algorithms for mining EPs, namely MERIT, dMERIT +, MEI and EIFDD. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 134415448 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: SPPC: a new tree structure for mining erasable patterns in data streams. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Le%2C+Tuong%22">Le, Tuong</searchLink><relatesTo>1</relatesTo><i> tuonglc@sju.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Vo%2C+Bay%22">Vo, Bay</searchLink><relatesTo>2,3</relatesTo><i> vodinhbay@tdtu.edu.vn</i><br /><searchLink fieldCode="AR" term="%22Fournier-Viger%2C+Philippe%22">Fournier-Viger, Philippe</searchLink><relatesTo>4</relatesTo><i> philfv@hit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Mi+Young%22">Lee, Mi Young</searchLink><relatesTo>1</relatesTo><i> miylee@sejong.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Baik%2C+Sung+Wook%22">Baik, Sung Wook</searchLink><relatesTo>1</relatesTo><i> sbaik@sejong.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Feb2019, Vol. 49 Issue 2, p478-495. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Erasable+programmable+read-only+memory%22">Erasable programmable read-only memory</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Planography%22">Planography</searchLink><br /><searchLink fieldCode="DE" term="%22Meta-analysis%22">Meta-analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Discovering Erasable Patterns (EPs) consists of identifying product parts that will produce a small profit loss if their production is stopped. It is a data mining problem that has attracted the attention of numerous researchers in recent years due to the possibility of using EPs to reduce profit loss of manufacturers. Though, many algorithms have been designed to mine EPs, an important limitation of state-of-the-art EP mining algorithms is that they are batch algorithms, that is, they are designed to be applied on static databases. But in real-life applications, databases are dynamic, as they are constantly updated by adding or removing products and parts. To be informed about EPs in real-time, traditional EP mining algorithms must be applied over and over again on a database. This is inefficient as those algorithms are always applied from scratch without taking advantage of results generated by previous executions. Considering this important drawback of previous work for handling real-life dynamic data, this paper proposes an efficient algorithm named MSPPC for mining EPs in data streams. It relies on a novel tree structure named SPPC (Streaming Pre-Post Code) tree, which extends the WPPC tree structure for maintaining a compact tree representation of EPs in a data stream. Experimental results show that the designed MSPPC algorithm outperforms the state-of-the-art batch MERIT and dMERIT algorithms when they are run in batch mode using a sliding-window. Besides, the proposed algorithm is also faster than the state-of-the-art algorithms for mining EPs, namely MERIT, dMERIT +, MEI and EIFDD. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Intelligence 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/s10489-018-1280-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 478 Subjects: – SubjectFull: Erasable programmable read-only memory Type: general – SubjectFull: Data mining Type: general – SubjectFull: Decision making Type: general – SubjectFull: Planography Type: general – SubjectFull: Meta-analysis Type: general Titles: – TitleFull: SPPC: a new tree structure for mining erasable patterns in data streams. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Le, Tuong – PersonEntity: Name: NameFull: Vo, Bay – PersonEntity: Name: NameFull: Fournier-Viger, Philippe – PersonEntity: Name: NameFull: Lee, Mi Young – PersonEntity: Name: NameFull: Baik, Sung Wook IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 49 – Type: issue Value: 2 Titles: – TitleFull: Applied Intelligence Type: main |
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