Apriori-roaring: frequent pattern mining method based on compressed bitmaps
Association rule mining is one of the most common tasks in data analysis. It has a descriptive feature used to discover patterns in sets of data. Most existing approaches to data analysis have a constraint related to execution time. However, as the size of datasets used in the analysis grows, memory...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/241304 |
| Acceso en línea: | http://dx.doi.org/10.1504/IJBIDM.2022.123805 http://hdl.handle.net/11449/241304 |
| Access Level: | acceso abierto |
| Palabra clave: | association rules bitmap compression data mining frequent pattern mining knowledge discovery |
| Sumario: | Association rule mining is one of the most common tasks in data analysis. It has a descriptive feature used to discover patterns in sets of data. Most existing approaches to data analysis have a constraint related to execution time. However, as the size of datasets used in the analysis grows, memory usage tends to be the constraint instead, and this prevents these approaches from being used. This article presents a new method for data analysis called apriori-roaring. The apriori-roaring method is designed to identify frequent items with a focus on scalability. The implementation of this method employs compressed bitmap structures, which use less memory to store the original dataset and to calculate the support metric. The results show that apriori-roaring allows the identification of frequent elements with much lower memory usage and shorter execution time. In terms of scalability, our proposed approach outperforms the various traditional approaches available. |
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