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Stable top-k periodic high-utility patterns mining over multi-sequence.

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Title: Stable top-k periodic high-utility patterns mining over multi-sequence.
Authors: Ren, Ziqian1 (AUTHOR), Xun, Yaling1,2 (AUTHOR) xunyl55@126.com, Cai, Jianghui1 (AUTHOR), Yang, Haifeng1,2 (AUTHOR)
Source: Intelligent Data Analysis. Mar2025, Vol. 29 Issue 2, p351-371. 21p.
Subjects: Sequential pattern mining, Data mining, Algorithms, Decision making
Abstract: Periodic high-utility sequential patterns (PHUSPs) mining is one of the research hotspots in data mining, which aims to discover patterns that not only have high utility but also regularly appear in sequence datasets. Traditional PHUSP mining mainly focuses on mining patterns from a single sequence, which often results in some interesting patterns being discarded due to strict constraints, and most of the discovered patterns are unstable and difficult to use for decision-making. In response to this issue, a novel algorithm called TKSPUS (top-k stable periodic high-utility sequential pattern mining) is proposed to discover stable top-k periodic high-utility sequential patterns that co-occur in multi-sequences. TKSPUS extends the traditional periodic high-utility sequential patterns mining, and designs two new metrics, namely utility stability coefficient (usc) and periodic stability coefficient (sr), to determine the periodic stability and utility stability of patterns in multi-sequences respectively. Additionally, the TKSPUS algorithm adopts the projection mechanism to mine stable periodic high-utility patterns over multi-sequence, while a new data structure called pusc and two corresponding pruning strategies are also introduced to boost the mining process. Experiments show that compared with the other four related algorithms, the TKSPUS algorithm has better performance in memory consumption and execution time, and the stability of the mining results is improved by 47% on average compared with the traditional periodic high-utility patterns mining algorithm. [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.)
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  Data: Stable top-k periodic high-utility patterns mining over multi-sequence.
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  Data: <searchLink fieldCode="JN" term="%22Intelligent+Data+Analysis%22">Intelligent Data Analysis</searchLink>. Mar2025, Vol. 29 Issue 2, p351-371. 21p.
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Periodic high-utility sequential patterns (PHUSPs) mining is one of the research hotspots in data mining, which aims to discover patterns that not only have high utility but also regularly appear in sequence datasets. Traditional PHUSP mining mainly focuses on mining patterns from a single sequence, which often results in some interesting patterns being discarded due to strict constraints, and most of the discovered patterns are unstable and difficult to use for decision-making. In response to this issue, a novel algorithm called TKSPUS (top-k stable periodic high-utility sequential pattern mining) is proposed to discover stable top-k periodic high-utility sequential patterns that co-occur in multi-sequences. TKSPUS extends the traditional periodic high-utility sequential patterns mining, and designs two new metrics, namely utility stability coefficient (usc) and periodic stability coefficient (sr), to determine the periodic stability and utility stability of patterns in multi-sequences respectively. Additionally, the TKSPUS algorithm adopts the projection mechanism to mine stable periodic high-utility patterns over multi-sequence, while a new data structure called pusc and two corresponding pruning strategies are also introduced to boost the mining process. Experiments show that compared with the other four related algorithms, the TKSPUS algorithm has better performance in memory consumption and execution time, and the stability of the mining results is improved by 47% on average compared with the traditional periodic high-utility patterns mining algorithm. [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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        Value: 10.3233/IDA-230672
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      – Code: eng
        Text: English
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        StartPage: 351
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        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Algorithms
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      – SubjectFull: Decision making
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            – D: 01
              M: 03
              Text: Mar2025
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              Y: 2025
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