Incremental Fuzzy Association Rule Mining for Classification and Regression
The aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets. With the arrival of dynamic data, the fuzzy association rules should be updated in real time. However, most of the existing algorithms must remine the updated database and can...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8788644/ |
| _version_ | 1818603318615736320 |
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| author | Ling Wang Qian Ma Jianyao Meng |
| author_facet | Ling Wang Qian Ma Jianyao Meng |
| author_sort | Ling Wang |
| collection | DOAJ |
| description | The aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets. With the arrival of dynamic data, the fuzzy association rules should be updated in real time. However, most of the existing algorithms must remine the updated database and can only be applied in classification. This paper proposes an incremental fuzzy association rule mining algorithm to solve classification and regression problems. First, the sliding window is adopted to divide the fuzzy dataset. Second, the dynamic fuzzy variable selection algorithm is adopted to select variables for reducing the search space of the fuzzy association rule mining. Finally, in each sliding window, the result of variable selection is used to incrementally mine the causal fuzzy association rules with the fuzzy Eclat algorithm. When new data are added, the process judges whether concept drift occurs, and if so, the rule set is updated; otherwise, the original rule set is still applied. The weights of the rules are calculated to find the evolving relationship. The simulation result shows that this algorithm can improve accuracy and efficiency. |
| first_indexed | 2024-12-16T13:21:16Z |
| format | Article |
| id | doaj.art-43eb9219c204424e8b88a7bb6635a602 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:21:16Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-43eb9219c204424e8b88a7bb6635a6022022-12-21T22:30:20ZengIEEEIEEE Access2169-35362019-01-01712109512111010.1109/ACCESS.2019.29333618788644Incremental Fuzzy Association Rule Mining for Classification and RegressionLing Wang0https://orcid.org/0000-0003-4098-7906Qian Ma1Jianyao Meng2School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, ChinaThe aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets. With the arrival of dynamic data, the fuzzy association rules should be updated in real time. However, most of the existing algorithms must remine the updated database and can only be applied in classification. This paper proposes an incremental fuzzy association rule mining algorithm to solve classification and regression problems. First, the sliding window is adopted to divide the fuzzy dataset. Second, the dynamic fuzzy variable selection algorithm is adopted to select variables for reducing the search space of the fuzzy association rule mining. Finally, in each sliding window, the result of variable selection is used to incrementally mine the causal fuzzy association rules with the fuzzy Eclat algorithm. When new data are added, the process judges whether concept drift occurs, and if so, the rule set is updated; otherwise, the original rule set is still applied. The weights of the rules are calculated to find the evolving relationship. The simulation result shows that this algorithm can improve accuracy and efficiency.https://ieeexplore.ieee.org/document/8788644/Fuzzy association rulesincrementalclassificationregressionEclat algorithm |
| spellingShingle | Ling Wang Qian Ma Jianyao Meng Incremental Fuzzy Association Rule Mining for Classification and Regression IEEE Access Fuzzy association rules incremental classification regression Eclat algorithm |
| title | Incremental Fuzzy Association Rule Mining for Classification and Regression |
| title_full | Incremental Fuzzy Association Rule Mining for Classification and Regression |
| title_fullStr | Incremental Fuzzy Association Rule Mining for Classification and Regression |
| title_full_unstemmed | Incremental Fuzzy Association Rule Mining for Classification and Regression |
| title_short | Incremental Fuzzy Association Rule Mining for Classification and Regression |
| title_sort | incremental fuzzy association rule mining for classification and regression |
| topic | Fuzzy association rules incremental classification regression Eclat algorithm |
| url | https://ieeexplore.ieee.org/document/8788644/ |
| work_keys_str_mv | AT lingwang incrementalfuzzyassociationruleminingforclassificationandregression AT qianma incrementalfuzzyassociationruleminingforclassificationandregression AT jianyaomeng incrementalfuzzyassociationruleminingforclassificationandregression |