Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data Article Swipe
Aihua Zhu
,
Haote Zhang
,
Xingqian Chen
,
Dingkun Zhu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13081349
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13081349
This paper introduces a dual-strategy model based on temporal transformation and fuzzy theory, and designs a partitioned mining algorithm for periodic frequent patterns in large-scale event data (3P-TFT). The model reconstructs original event data through temporal reorganization and attribute fuzzification, preserving data continuity distribution characteristics while enabling efficient processing of multidimensional attributes within a multi-temporal granularity calendar framework. The 3P-TFT algorithm employs temporal interval and object attribute partitioning strategies to achieve distributed mining of large-scale data. Experimental results demonstrate that this method effectively reveals hidden periodic patterns in stock trading events at specific temporal granularities, with volume–price association rules providing significant predictive and decision-making value. Furthermore, comparative algorithm experiments confirm that the 3P-TFT algorithm exhibits exceptional stability and adaptability across event databases with various cycle lengths, offering a novel theoretical tool for complex event data mining.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math13081349
- https://www.mdpi.com/2227-7390/13/8/1349/pdf?version=1745166416
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4409606274Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math13081349Digital Object Identifier
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Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event DataWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-20Full publication date if available
- Authors
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Aihua Zhu, Haote Zhang, Xingqian Chen, Dingkun ZhuList of authors in order
- Landing page
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https://doi.org/10.3390/math13081349Publisher landing page
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https://www.mdpi.com/2227-7390/13/8/1349/pdf?version=1745166416Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/13/8/1349/pdf?version=1745166416Direct OA link when available
- Concepts
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Event (particle physics), Data mining, Decomposition, Computer science, Fuzzy logic, Algorithm, Event data, Block (permutation group theory), Artificial intelligence, Mathematics, Physics, Biology, Combinatorics, Analytics, Quantum mechanics, EcologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2558459800, https://openalex.org/W2234838258, https://openalex.org/W4206103053, https://openalex.org/W4386261323, https://openalex.org/W4226218669, https://openalex.org/W4387771787, https://openalex.org/W1967430507, https://openalex.org/W1505121069, https://openalex.org/W3008560783, https://openalex.org/W4389850330, https://openalex.org/W4320151832, https://openalex.org/W4387789933, https://openalex.org/W3082637824, https://openalex.org/W3083619885, https://openalex.org/W2668106230, https://openalex.org/W4360884738, https://openalex.org/W2036045038, https://openalex.org/W2141145012, https://openalex.org/W4308740912, https://openalex.org/W2501520615, https://openalex.org/W3008429170, https://openalex.org/W3036827731, https://openalex.org/W2307888838, https://openalex.org/W2144267426, https://openalex.org/W1724920484, https://openalex.org/W2088948548, https://openalex.org/W4285219115, https://openalex.org/W2003012477, https://openalex.org/W2171488831, https://openalex.org/W4317725806, https://openalex.org/W4280518412 |
| referenced_works_count | 31 |
| abstract_inverted_index.a | 3, 15, 53, 127 |
| abstract_inverted_index.at | 91 |
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| abstract_inverted_index.of | 49, 73 |
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| abstract_inverted_index.and | 10, 13, 37, 64, 102, 117 |
| abstract_inverted_index.for | 19, 131 |
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| abstract_inverted_index.data. | 75 |
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| abstract_inverted_index.stock | 88 |
| abstract_inverted_index.while | 45 |
| abstract_inverted_index.3P-TFT | 59, 112 |
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| abstract_inverted_index.results | 77 |
| abstract_inverted_index.reveals | 83 |
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| abstract_inverted_index.specific | 92 |
| abstract_inverted_index.temporal | 8, 35, 62, 93 |
| abstract_inverted_index.(3P-TFT). | 27 |
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| abstract_inverted_index.attribute | 38, 66 |
| abstract_inverted_index.databases | 121 |
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| abstract_inverted_index.framework. | 57 |
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| abstract_inverted_index.Furthermore, | 105 |
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| abstract_inverted_index.partitioning | 67 |
| abstract_inverted_index.reconstructs | 30 |
| abstract_inverted_index.dual-strategy | 4 |
| abstract_inverted_index.fuzzification, | 39 |
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| abstract_inverted_index.multi-temporal | 54 |
| abstract_inverted_index.reorganization | 36 |
| abstract_inverted_index.transformation | 9 |
| abstract_inverted_index.volume–price | 96 |
| abstract_inverted_index.characteristics | 44 |
| abstract_inverted_index.decision-making | 103 |
| abstract_inverted_index.multidimensional | 50 |
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| citation_normalized_percentile.is_in_top_10_percent | True |