Research on Fuzzy Temporal Event Association Mining Model and Algorithm
As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the te...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/2/117 |
| _version_ | 1797622429387849728 |
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| author | Aihua Zhu Zhiqing Meng Rui Shen |
| author_facet | Aihua Zhu Zhiqing Meng Rui Shen |
| author_sort | Aihua Zhu |
| collection | DOAJ |
| description | As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data. |
| first_indexed | 2024-03-11T09:10:09Z |
| format | Article |
| id | doaj.art-96b84d17f81242a78848e2de6a71f045 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-11T09:10:09Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-96b84d17f81242a78848e2de6a71f0452023-11-16T19:05:30ZengMDPI AGAxioms2075-16802023-01-0112211710.3390/axioms12020117Research on Fuzzy Temporal Event Association Mining Model and AlgorithmAihua Zhu0Zhiqing Meng1Rui Shen2School of Management, Zhejiang University of Technology, Hangzhou 310023, ChinaSchool of Management, Zhejiang University of Technology, Hangzhou 310023, ChinaSchool of Economics, Zhejiang University of Technology, Hangzhou 310023, ChinaAs traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.https://www.mdpi.com/2075-1680/12/2/117fuzzy temporal data miningfuzzy temporal association rulesfuzzy temporal eventtemporal type |
| spellingShingle | Aihua Zhu Zhiqing Meng Rui Shen Research on Fuzzy Temporal Event Association Mining Model and Algorithm Axioms fuzzy temporal data mining fuzzy temporal association rules fuzzy temporal event temporal type |
| title | Research on Fuzzy Temporal Event Association Mining Model and Algorithm |
| title_full | Research on Fuzzy Temporal Event Association Mining Model and Algorithm |
| title_fullStr | Research on Fuzzy Temporal Event Association Mining Model and Algorithm |
| title_full_unstemmed | Research on Fuzzy Temporal Event Association Mining Model and Algorithm |
| title_short | Research on Fuzzy Temporal Event Association Mining Model and Algorithm |
| title_sort | research on fuzzy temporal event association mining model and algorithm |
| topic | fuzzy temporal data mining fuzzy temporal association rules fuzzy temporal event temporal type |
| url | https://www.mdpi.com/2075-1680/12/2/117 |
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