Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework
Data mining has become a popular process in recent times. However, with the increase in data, traditional data mining methods are not sufficient to solve many problems. Therefore, advanced techniques are needed to provide better results without consuming more time during execution. Soft computing al...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10210547/ |
| _version_ | 1797741574242697216 |
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| author | Mrinalini Rana Omdev Dahiya Parminder Singh Wadii Boulila Adel Ammar |
| author_facet | Mrinalini Rana Omdev Dahiya Parminder Singh Wadii Boulila Adel Ammar |
| author_sort | Mrinalini Rana |
| collection | DOAJ |
| description | Data mining has become a popular process in recent times. However, with the increase in data, traditional data mining methods are not sufficient to solve many problems. Therefore, advanced techniques are needed to provide better results without consuming more time during execution. Soft computing algorithms are used for mathematical optimization to achieve better results in less time. The primary purpose of this work is to propose a framework for rule mining that shall generalize the currently applied methods in rule mining. In this respect, this paper represents the R-miner using a soft computing algorithm. The Grouped -Artificial Bee Colony Optimization (G-ABC) was used to select the relevant attribute set and further verify the features. Mean-Variance optimization is used to find whether the selected rule is valid for further classification. Furthermore, a neural-based deep learning method is applied to validate the outcome. The investigation outcome indicates that the proposed algorithm provides more optimized results in terms of the number of rules generated, the time required for calculation, and obtaining supplementary information for rule mining. |
| first_indexed | 2024-03-12T14:28:40Z |
| format | Article |
| id | doaj.art-be97168d3b3240e98333dce84da0addc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:28:40Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-be97168d3b3240e98333dce84da0addc2023-08-17T23:00:16ZengIEEEIEEE Access2169-35362023-01-0111857478575910.1109/ACCESS.2023.330336010210547Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid FrameworkMrinalini Rana0https://orcid.org/0000-0003-2597-5675Omdev Dahiya1https://orcid.org/0000-0003-2245-2692Parminder Singh2https://orcid.org/0000-0002-0750-6309Wadii Boulila3https://orcid.org/0000-0003-2133-0757Adel Ammar4https://orcid.org/0000-0003-0795-132XSchool of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, IndiaSchool of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, IndiaSchool of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, IndiaRobotics and Internet of Things Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaRobotics and Internet of Things Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaData mining has become a popular process in recent times. However, with the increase in data, traditional data mining methods are not sufficient to solve many problems. Therefore, advanced techniques are needed to provide better results without consuming more time during execution. Soft computing algorithms are used for mathematical optimization to achieve better results in less time. The primary purpose of this work is to propose a framework for rule mining that shall generalize the currently applied methods in rule mining. In this respect, this paper represents the R-miner using a soft computing algorithm. The Grouped -Artificial Bee Colony Optimization (G-ABC) was used to select the relevant attribute set and further verify the features. Mean-Variance optimization is used to find whether the selected rule is valid for further classification. Furthermore, a neural-based deep learning method is applied to validate the outcome. The investigation outcome indicates that the proposed algorithm provides more optimized results in terms of the number of rules generated, the time required for calculation, and obtaining supplementary information for rule mining.https://ieeexplore.ieee.org/document/10210547/Rule miningfeature selectionparticle swarm optimizationartificial bee colony optimization |
| spellingShingle | Mrinalini Rana Omdev Dahiya Parminder Singh Wadii Boulila Adel Ammar Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework IEEE Access Rule mining feature selection particle swarm optimization artificial bee colony optimization |
| title | Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework |
| title_full | Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework |
| title_fullStr | Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework |
| title_full_unstemmed | Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework |
| title_short | Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework |
| title_sort | grouped abc for feature selection and mean variance optimization for rule mining a hybrid framework |
| topic | Rule mining feature selection particle swarm optimization artificial bee colony optimization |
| url | https://ieeexplore.ieee.org/document/10210547/ |
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