Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data
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 fuzzif...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/8/1349 |
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| author | Aihua Zhu Haote Zhang Xingqian Chen Dingkun Zhu |
| author_facet | Aihua Zhu Haote Zhang Xingqian Chen Dingkun Zhu |
| author_sort | Aihua Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e7656d73e8f94f5fa274e832a47546d7 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-e7656d73e8f94f5fa274e832a47546d72025-08-20T02:28:24ZengMDPI AGMathematics2227-73902025-04-01138134910.3390/math13081349Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event DataAihua Zhu0Haote Zhang1Xingqian Chen2Dingkun Zhu3School of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, ChinaSchool of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, ChinaSchool of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, ChinaSchool of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, ChinaThis 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.https://www.mdpi.com/2227-7390/13/8/1349temporal data miningfuzzy temporal association rulesperiodic frequent patternsdistributed mining |
| spellingShingle | Aihua Zhu Haote Zhang Xingqian Chen Dingkun Zhu Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data Mathematics temporal data mining fuzzy temporal association rules periodic frequent patterns distributed mining |
| title | Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data |
| title_full | Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data |
| title_fullStr | Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data |
| title_full_unstemmed | Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data |
| title_short | Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data |
| title_sort | multiscale fuzzy temporal pattern mining a block decomposition algorithm for partial periodic associations in event data |
| topic | temporal data mining fuzzy temporal association rules periodic frequent patterns distributed mining |
| url | https://www.mdpi.com/2227-7390/13/8/1349 |
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