Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns
The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper propo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/6/738 |
| _version_ | 1797530529637072896 |
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| author | Shenghan Zhou Houxiang Liu Bang Chen Wenkui Hou Xinpeng Ji Yue Zhang Wenbing Chang Yiyong Xiao |
| author_facet | Shenghan Zhou Houxiang Liu Bang Chen Wenkui Hou Xinpeng Ji Yue Zhang Wenbing Chang Yiyong Xiao |
| author_sort | Shenghan Zhou |
| collection | DOAJ |
| description | The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system. |
| first_indexed | 2024-03-10T10:30:18Z |
| format | Article |
| id | doaj.art-6a2f85d684a34189a0a72bf8542d4b32 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T10:30:18Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-6a2f85d684a34189a0a72bf8542d4b322023-11-21T23:41:41ZengMDPI AGEntropy1099-43002021-06-0123673810.3390/e23060738Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of PatternsShenghan Zhou0Houxiang Liu1Bang Chen2Wenkui Hou3Xinpeng Ji4Yue Zhang5Wenbing Chang6Yiyong Xiao7School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaThe traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.https://www.mdpi.com/1099-4300/23/6/738data miningstatus set sequential pattern miningtime windowTW-Apriori algorithmperiodicity analysis |
| spellingShingle | Shenghan Zhou Houxiang Liu Bang Chen Wenkui Hou Xinpeng Ji Yue Zhang Wenbing Chang Yiyong Xiao Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns Entropy data mining status set sequential pattern mining time window TW-Apriori algorithm periodicity analysis |
| title | Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns |
| title_full | Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns |
| title_fullStr | Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns |
| title_full_unstemmed | Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns |
| title_short | Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns |
| title_sort | status set sequential pattern mining considering time windows and periodic analysis of patterns |
| topic | data mining status set sequential pattern mining time window TW-Apriori algorithm periodicity analysis |
| url | https://www.mdpi.com/1099-4300/23/6/738 |
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