Discovering Patterns With Weak-Wildcard Gaps
Time series analysis is an important data mining task in areas such as the stock market and petroleum industry. One interesting problem in knowledge discovery is the detection of previously unknown frequent patterns. With the existing types of patterns, some similar subsequences are overlooked or di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2016-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7552501/ |
| _version_ | 1818411547127447552 |
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| author | Chao-Dong Tan Fan Min Min Wang Heng-Ru Zhang Zhi-Heng Zhang |
| author_facet | Chao-Dong Tan Fan Min Min Wang Heng-Ru Zhang Zhi-Heng Zhang |
| author_sort | Chao-Dong Tan |
| collection | DOAJ |
| description | Time series analysis is an important data mining task in areas such as the stock market and petroleum industry. One interesting problem in knowledge discovery is the detection of previously unknown frequent patterns. With the existing types of patterns, some similar subsequences are overlooked or dissimilar ones are matched. In this paper, we define patterns with weak-wildcard gaps to represent subsequences with noise and shift, and design efficient algorithms to obtain frequent and strong patterns. First, we convert a numeric time series into a sequence according to the data fluctuation. Second, we define the pattern mining with weak-wildcard gaps problem, where a weak-wildcard matches any character in an alphabet subset. Third, we design an Apriori-like algorithm with an efficient pruning technique to obtain frequent and strong patterns. Experimental results show that our algorithm is efficient and can discover frequent and strong patterns. |
| first_indexed | 2024-12-14T10:33:09Z |
| format | Article |
| id | doaj.art-a2c8219376e649d8abfd04df72244485 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:33:09Z |
| publishDate | 2016-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a2c8219376e649d8abfd04df722444852022-12-21T23:06:04ZengIEEEIEEE Access2169-35362016-01-0144922493210.1109/ACCESS.2016.25939537552501Discovering Patterns With Weak-Wildcard GapsChao-Dong Tan0Fan Min1https://orcid.org/0000-0002-3290-1036Min Wang2Heng-Ru Zhang3Zhi-Heng Zhang4College of Petroleum Engineering, China University of Petroleum, Beijing, ChinaCollege of Petroleum Engineering, China University of Petroleum, Beijing, ChinaCollege of Petroleum Engineering, China University of Petroleum, Beijing, ChinaCollege of Petroleum Engineering, China University of Petroleum, Beijing, ChinaCollege of Petroleum Engineering, China University of Petroleum, Beijing, ChinaTime series analysis is an important data mining task in areas such as the stock market and petroleum industry. One interesting problem in knowledge discovery is the detection of previously unknown frequent patterns. With the existing types of patterns, some similar subsequences are overlooked or dissimilar ones are matched. In this paper, we define patterns with weak-wildcard gaps to represent subsequences with noise and shift, and design efficient algorithms to obtain frequent and strong patterns. First, we convert a numeric time series into a sequence according to the data fluctuation. Second, we define the pattern mining with weak-wildcard gaps problem, where a weak-wildcard matches any character in an alphabet subset. Third, we design an Apriori-like algorithm with an efficient pruning technique to obtain frequent and strong patterns. Experimental results show that our algorithm is efficient and can discover frequent and strong patterns.https://ieeexplore.ieee.org/document/7552501/Pattern discoverysequencestime series analysisweak-wildcard |
| spellingShingle | Chao-Dong Tan Fan Min Min Wang Heng-Ru Zhang Zhi-Heng Zhang Discovering Patterns With Weak-Wildcard Gaps IEEE Access Pattern discovery sequences time series analysis weak-wildcard |
| title | Discovering Patterns With Weak-Wildcard Gaps |
| title_full | Discovering Patterns With Weak-Wildcard Gaps |
| title_fullStr | Discovering Patterns With Weak-Wildcard Gaps |
| title_full_unstemmed | Discovering Patterns With Weak-Wildcard Gaps |
| title_short | Discovering Patterns With Weak-Wildcard Gaps |
| title_sort | discovering patterns with weak wildcard gaps |
| topic | Pattern discovery sequences time series analysis weak-wildcard |
| url | https://ieeexplore.ieee.org/document/7552501/ |
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