Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm
As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule alg...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/1446405 |
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| author | Qunfeng Wei Bin Qi |
| author_facet | Qunfeng Wei Bin Qi |
| author_sort | Qunfeng Wei |
| collection | DOAJ |
| description | As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis. |
| format | Article |
| id | doaj-art-5c4ebc2808b646e2bddb70f6df1f3602 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-5c4ebc2808b646e2bddb70f6df1f36022025-02-03T01:23:35ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/1446405Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis AlgorithmQunfeng Wei0Bin Qi1College of Electronic InformationCollege of Electronic InformationAs a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.http://dx.doi.org/10.1155/2022/1446405 |
| spellingShingle | Qunfeng Wei Bin Qi Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm International Transactions on Electrical Energy Systems |
| title | Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm |
| title_full | Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm |
| title_fullStr | Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm |
| title_full_unstemmed | Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm |
| title_short | Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm |
| title_sort | neutrosophic fuzzy association rule generation based big data mining analysis algorithm |
| url | http://dx.doi.org/10.1155/2022/1446405 |
| work_keys_str_mv | AT qunfengwei neutrosophicfuzzyassociationrulegenerationbasedbigdatamininganalysisalgorithm AT binqi neutrosophicfuzzyassociationrulegenerationbasedbigdatamininganalysisalgorithm |