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Boosting association rule mining in large datasets via Gibbs sampling
Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this paper, we dev...
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| Publicado no: | Proc Natl Acad Sci U S A |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
National Academy of Sciences
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4983808/ https://ncbi.nlm.nih.gov/pubmed/27091963 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1604553113 |
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