Processamento Eficiente de Regras de Associação que Utilizam as Preferências dos Usuários: Uma Proposta
Resumo
A descoberta de padrões em bancos de dados transacionais é um assunto bem explorado e muitos métodos têm sido propostos para resolver problema. Um dos métodos mais conhecidos é a mineração por associação regras. No entanto, é difícil selecionar as melhores regras usando esse método, requer um bom número de apoio e confiança, o que não é fácil conjunto. Para superar esse problema, os métodos de regras de mineração baseados em preferências foram propostas. Eles ajudam no processo de encontrar as melhores regras, em conta a preferência dos usuários. Infelizmente, esses métodos são consome memória para processar. Neste artigo, apresentamos novos algoritmos para processamento de regras de mineração baseadas em preferências de forma eficiente.
Referências
Abaya, S. A. (2012). Association rule mining based on apriori algorithm in minimizing candidate generation. International Journal of Scientific & Engineering Research, 3(7):1–4.
Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, volume 1215, pages 487–499.
Altaf, W., Shahbaz, M., and Guergachi, A. (2017). Applications of association rule mining in health informatics: a survey. Artificial Intelligence Review, 47(3):313–340.
Boja, C., Pocovnicu, A., and Batagan, L. (2012). Distributed parallel architecture for”big data”. Informatica Economica, 16(2):116.
Bouker, S., Saidi, R., Yahia, S. B., and Nguifo, E. M. (2012). Ranking and selecting association rules based on dominance relationship. In Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on, volume 1, pages 658–665. IEEE.
Bouker, S., Saidi, R., Yahia, S. B., and Nguifo, E. M. (2013). Towards a semantic and statistical selection of association rules. arXiv preprint arXiv:1305.5824.
Dahbi, A., Jabri, S., Balouki, Y., and Gadi, T. (2016). A new method for ranking association rules with multiple criteria based on dominance relation. In Computer Systems and Applications (AICCSA), 2016 IEEE/ACS 13th International Conference of, pages 1–7. IEEE.
Koh, J.-L., Lin, C.-Y., and Chen, A. L. (2014). Finding k most favorite products based on reverse top-t queries. The VLDB Journal, 23(4):541–564.
Mohammed, M., Taoufiq, G., Youssef, B., et al. (2015). A new way to select the valuable association rules. In Knowledge and Smart Technology (KST), 2015 7th International Conference on, pages 81–86. IEEE.
Rocha-Junior, J. B. (2013). Efficient Processing of Preference Queries in Distributed and Spatial Databases. PhD thesis, Norwegian University of Science and Technology, Trondheim, Norway.
Sahoo, J., Das, A. K., and Goswami, A. (2015). An efficient approach for mining association rules from high utility itemsets. Expert Systems with Applications, 42(13):5754–5778.
Tran, A., Truong, T., and Le, B. (2017). Efficiently mining association rules based on maximum single constraints. Vietnam Journal of Computer Science, pages 1–17.
Villars, R. L., Olofson, C. W., and Eastwood, M. (2011). Big data: What it is and why you should care. White Paper, IDC, 14.
Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, volume 1215, pages 487–499.
Altaf, W., Shahbaz, M., and Guergachi, A. (2017). Applications of association rule mining in health informatics: a survey. Artificial Intelligence Review, 47(3):313–340.
Boja, C., Pocovnicu, A., and Batagan, L. (2012). Distributed parallel architecture for”big data”. Informatica Economica, 16(2):116.
Bouker, S., Saidi, R., Yahia, S. B., and Nguifo, E. M. (2012). Ranking and selecting association rules based on dominance relationship. In Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on, volume 1, pages 658–665. IEEE.
Bouker, S., Saidi, R., Yahia, S. B., and Nguifo, E. M. (2013). Towards a semantic and statistical selection of association rules. arXiv preprint arXiv:1305.5824.
Dahbi, A., Jabri, S., Balouki, Y., and Gadi, T. (2016). A new method for ranking association rules with multiple criteria based on dominance relation. In Computer Systems and Applications (AICCSA), 2016 IEEE/ACS 13th International Conference of, pages 1–7. IEEE.
Koh, J.-L., Lin, C.-Y., and Chen, A. L. (2014). Finding k most favorite products based on reverse top-t queries. The VLDB Journal, 23(4):541–564.
Mohammed, M., Taoufiq, G., Youssef, B., et al. (2015). A new way to select the valuable association rules. In Knowledge and Smart Technology (KST), 2015 7th International Conference on, pages 81–86. IEEE.
Rocha-Junior, J. B. (2013). Efficient Processing of Preference Queries in Distributed and Spatial Databases. PhD thesis, Norwegian University of Science and Technology, Trondheim, Norway.
Sahoo, J., Das, A. K., and Goswami, A. (2015). An efficient approach for mining association rules from high utility itemsets. Expert Systems with Applications, 42(13):5754–5778.
Tran, A., Truong, T., and Le, B. (2017). Efficiently mining association rules based on maximum single constraints. Vietnam Journal of Computer Science, pages 1–17.
Villars, R. L., Olofson, C. W., and Eastwood, M. (2011). Big data: What it is and why you should care. White Paper, IDC, 14.
Publicado
22/08/2018
Como Citar
COSTA, Rêude; JUNIOR, João.
Processamento Eficiente de Regras de Associação que Utilizam as Preferências dos Usuários: Uma Proposta. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 18. , 2018, Aracaju.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
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p. 422-427.
