Mining high utility itemsets based on the time decaying model.
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| Title: | Mining high utility itemsets based on the time decaying model. |
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| Authors: | Donggyu Kim1, Unil Yun1 yunei@sejong.ac.kr |
| Source: | Intelligent Data Analysis. 2016, Vol. 20 Issue 5, p1157-1180. 24p. |
| Subjects: | Algorithm software, Data mining, Streaming technology, Data structures, Online social networks |
| Abstract: | Various association rule mining techniques, such as frequent itemset mining, sequence itemset mining, and high utility itemset mining, have been studied to reveal valuable knowledge hidden from large databases. Among these techniques, high utility itemset mining has been researched actively by many researchers because of its characteristics that can find more meaningful itemsets compared to those of other approaches by considering the utility of each item in a given database. In recent years, mining high utility itemsets over data streams has emerged as an interesting topic because many users want to obtain valuable information from stream data, which are continually generated at rapid rates. However, in these environments, most of the previous high utility itemset mining methods cannot efficiently work in terms of both runtime and memory usage. In addition, since they conduct their mining processes without any consideration of transactions' arrival-time, it is hard for these methods to sufficiently fulfill the needs of users when they want to obtain only up to date, relevant information over data streams. In this paper, we propose a new tree-based algorithm that mines recent high utility itemsets over data streams. On the basis of the time decaying model, our algorithm diminishes the utilities of transactions according to their arrival-time in order to assign larger weights to recent data compared to those of older ones. Moreover, the algorithm regularly updates the utility information in its tree data structure and prunes the nodes with the utility values less than a user-specified minimum value. Thereby, the algorithm can maintain a reasonable memory usage bound by avoiding memory use that is unessential. Experimental results demonstrate that our algorithm can mine recent high utility itemsets from varying stream data while consuming smaller computational resources than those of the existing algorithms. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 118257124 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mining high utility itemsets based on the time decaying model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Donggyu+Kim%22">Donggyu Kim</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Unil+Yun%22">Unil Yun</searchLink><relatesTo>1</relatesTo><i> yunei@sejong.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Intelligent+Data+Analysis%22">Intelligent Data Analysis</searchLink>. 2016, Vol. 20 Issue 5, p1157-1180. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithm+software%22">Algorithm software</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Streaming+technology%22">Streaming technology</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Online+social+networks%22">Online social networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Various association rule mining techniques, such as frequent itemset mining, sequence itemset mining, and high utility itemset mining, have been studied to reveal valuable knowledge hidden from large databases. Among these techniques, high utility itemset mining has been researched actively by many researchers because of its characteristics that can find more meaningful itemsets compared to those of other approaches by considering the utility of each item in a given database. In recent years, mining high utility itemsets over data streams has emerged as an interesting topic because many users want to obtain valuable information from stream data, which are continually generated at rapid rates. However, in these environments, most of the previous high utility itemset mining methods cannot efficiently work in terms of both runtime and memory usage. In addition, since they conduct their mining processes without any consideration of transactions' arrival-time, it is hard for these methods to sufficiently fulfill the needs of users when they want to obtain only up to date, relevant information over data streams. In this paper, we propose a new tree-based algorithm that mines recent high utility itemsets over data streams. On the basis of the time decaying model, our algorithm diminishes the utilities of transactions according to their arrival-time in order to assign larger weights to recent data compared to those of older ones. Moreover, the algorithm regularly updates the utility information in its tree data structure and prunes the nodes with the utility values less than a user-specified minimum value. Thereby, the algorithm can maintain a reasonable memory usage bound by avoiding memory use that is unessential. Experimental results demonstrate that our algorithm can mine recent high utility itemsets from varying stream data while consuming smaller computational resources than those of the existing algorithms. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/IDA-160861 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1157 Subjects: – SubjectFull: Algorithm software Type: general – SubjectFull: Data mining Type: general – SubjectFull: Streaming technology Type: general – SubjectFull: Data structures Type: general – SubjectFull: Online social networks Type: general Titles: – TitleFull: Mining high utility itemsets based on the time decaying model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Donggyu Kim – PersonEntity: Name: NameFull: Unil Yun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 1088467X Numbering: – Type: volume Value: 20 – Type: issue Value: 5 Titles: – TitleFull: Intelligent Data Analysis Type: main |
| ResultId | 1 |
