An efficient algorithm for high utility sequential pattern mining over data streams based on sliding window model.
Saved in:
| Title: | An efficient algorithm for high utility sequential pattern mining over data streams based on sliding window model. |
|---|---|
| Authors: | Han, Meng1 (AUTHOR) Z15891192043@163.com, Zhang, Ruihua1 (AUTHOR), He, Feifei1 (AUTHOR), Meng, Fanxing1 (AUTHOR), Li, Chunpeng1 (AUTHOR) |
| Source: | Intelligent Data Analysis. May2025, Vol. 29 Issue 3, p673-698. 26p. |
| Subjects: | Sequential pattern mining, Digital technology, High technology industries, Data mining, Algorithms |
| Abstract: | In the era of the digital economy, the exploration of useful knowledge from data streams has garnered significant attention due to its wide-ranging applications. However, the rapid and infinite nature of data streams poses challenges for efficiently mining high utility sequential patterns, including strong spatio-temporal constraints and the combinatorial explosion of sequence data search spaces. To address this and adapt to a variety of application scenarios, this paper delves into the investigation and design of an efficient algorithm for high utility sequential pattern mining over data streams based on the sliding window model (HUSP_DS). This algorithm utilizes a projection mechanism within a sliding window to recursively search for all interesting patterns. Additionally, it introduces a novel structure called the dynamic utility index table, which stores information such as the utility and index positions of data stream sequences. Notably, this structure proves highly effective in recursive search processes and utility updates. Comprehensive experimentation, conducted on both real-world and synthetic datasets, have shown that the superior performance of the HUSP_DS algorithm compared to state-of-the-art algorithms. This superiority is particularly evident in terms of temporal and spatial efficiency. Furthermore, the algorithm demonstrates suitability for mining sliding windows of arbitrary sizes, showcasing stable scalability. [ABSTRACT FROM AUTHOR] |
| Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 185001944 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: An efficient algorithm for high utility sequential pattern mining over data streams based on sliding window model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Han%2C+Meng%22">Han, Meng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Z15891192043@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Ruihua%22">Zhang, Ruihua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Feifei%22">He, Feifei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Fanxing%22">Meng, Fanxing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Chunpeng%22">Li, Chunpeng</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Intelligent+Data+Analysis%22">Intelligent Data Analysis</searchLink>. May2025, Vol. 29 Issue 3, p673-698. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sequential+pattern+mining%22">Sequential pattern mining</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22High+technology+industries%22">High technology industries</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the era of the digital economy, the exploration of useful knowledge from data streams has garnered significant attention due to its wide-ranging applications. However, the rapid and infinite nature of data streams poses challenges for efficiently mining high utility sequential patterns, including strong spatio-temporal constraints and the combinatorial explosion of sequence data search spaces. To address this and adapt to a variety of application scenarios, this paper delves into the investigation and design of an efficient algorithm for high utility sequential pattern mining over data streams based on the sliding window model (HUSP_DS). This algorithm utilizes a projection mechanism within a sliding window to recursively search for all interesting patterns. Additionally, it introduces a novel structure called the dynamic utility index table, which stores information such as the utility and index positions of data stream sequences. Notably, this structure proves highly effective in recursive search processes and utility updates. Comprehensive experimentation, conducted on both real-world and synthetic datasets, have shown that the superior performance of the HUSP_DS algorithm compared to state-of-the-art algorithms. This superiority is particularly evident in terms of temporal and spatial efficiency. Furthermore, the algorithm demonstrates suitability for mining sliding windows of arbitrary sizes, showcasing stable scalability. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185001944 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/1088467X241301387 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 673 Subjects: – SubjectFull: Sequential pattern mining Type: general – SubjectFull: Digital technology Type: general – SubjectFull: High technology industries Type: general – SubjectFull: Data mining Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: An efficient algorithm for high utility sequential pattern mining over data streams based on sliding window model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Han, Meng – PersonEntity: Name: NameFull: Zhang, Ruihua – PersonEntity: Name: NameFull: He, Feifei – PersonEntity: Name: NameFull: Meng, Fanxing – PersonEntity: Name: NameFull: Li, Chunpeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1088467X Numbering: – Type: volume Value: 29 – Type: issue Value: 3 Titles: – TitleFull: Intelligent Data Analysis Type: main |
| ResultId | 1 |
