Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm
Association rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised c...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/13/6154 |
| _version_ | 1827688271669362688 |
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| author | Iván Fredy Jaramillo Javier Garzás Andrés Redchuk |
| author_facet | Iván Fredy Jaramillo Javier Garzás Andrés Redchuk |
| author_sort | Iván Fredy Jaramillo |
| collection | DOAJ |
| description | Association rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised classification variety. This research presents a new alternative for solving the numerical association rule-mining problem from an optimization perspective by using the VMO (Variable Mesh Optimization) meta-heuristic. This work includes the ability for classification when categorical data are available from a defined rule schema. Our technique implements an optimization process for the intervals of continuous variables, unlike others that discretize these types of variables. Some experiments were carried out with a real dataset to evaluate the quality of the rules obtained; in addition to this, this technique was compared with four population-based algorithms. The results show that this implementation is competitive in classification cases and has more satisfactory results for completely numerical data. |
| first_indexed | 2024-03-10T09:52:32Z |
| format | Article |
| id | doaj.art-219fff1b58b04efd86093fc72f355a82 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:52:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-219fff1b58b04efd86093fc72f355a822023-11-22T02:34:27ZengMDPI AGApplied Sciences2076-34172021-07-011113615410.3390/app11136154Numerical Association Rule Mining from a Defined Schema Using the VMO AlgorithmIván Fredy Jaramillo0Javier Garzás1Andrés Redchuk2Facultad de Ciencias de la Ingeniería, Universidad Técnica Estatal de Quevedo, Quevedo 120501, EcuadorEscuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, 28933 Madrid, SpainEscuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, 28933 Madrid, SpainAssociation rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised classification variety. This research presents a new alternative for solving the numerical association rule-mining problem from an optimization perspective by using the VMO (Variable Mesh Optimization) meta-heuristic. This work includes the ability for classification when categorical data are available from a defined rule schema. Our technique implements an optimization process for the intervals of continuous variables, unlike others that discretize these types of variables. Some experiments were carried out with a real dataset to evaluate the quality of the rules obtained; in addition to this, this technique was compared with four population-based algorithms. The results show that this implementation is competitive in classification cases and has more satisfactory results for completely numerical data.https://www.mdpi.com/2076-3417/11/13/6154association rulesdata miningquantitative association rulesVMO algorithm |
| spellingShingle | Iván Fredy Jaramillo Javier Garzás Andrés Redchuk Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm Applied Sciences association rules data mining quantitative association rules VMO algorithm |
| title | Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm |
| title_full | Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm |
| title_fullStr | Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm |
| title_full_unstemmed | Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm |
| title_short | Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm |
| title_sort | numerical association rule mining from a defined schema using the vmo algorithm |
| topic | association rules data mining quantitative association rules VMO algorithm |
| url | https://www.mdpi.com/2076-3417/11/13/6154 |
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