| DOI | Resolve DOI: https://doi.org/10.1109/BigData.2018.8622138 |
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| Author | Search for: Buffett, Scott1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2018 IEEE International Conference on Big Data (Big Data), December 10-13, 2018, Seattle, WA, USA |
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| Subject | high utility sequential pattern mining; sequential pattern mining; frequent pattern mining; candidate list maintenance |
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| Abstract | High utility sequential pattern mining (HUSPM) lends the aspect of item value or importance to sequential pattern mining by identifying patterns that comprise a significant level of utility in a database. This paper addresses the challenge of establishing upper bounds on future candidate pattern utilities in an effort to reduce the search space required to identify the full set of patterns, and proposes a new approach where a list of possible candidate concatenation items is maintained. This list specifies the only items that ever need to be considered as possible candidates for concatenation with a sequential pattern being considered, or any future sequential pattern appearing as a descendant in the search tree. As a result of the elimination of items that are known to have no possibility of appearing in future high utility sequential patterns, an approach is presented that exploits this knowledge and computes a significantly tighter upper bound on the utilities of the such patterns. Tests on a variety of publicly available datasets show a dramatic reduction in the number of candidates considered, and the time taken to identify the full set of high utility sequential patterns is significantly reduced accordingly. |
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| Publication date | 2019-01-24 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | a0ed1a26-3195-4696-8254-6c2103e857e8 |
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| Record created | 2019-05-14 |
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| Record modified | 2022-02-21 |
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