Paralelização de Geração de Regras de Associação
Resumo
Mineração de dados é uma área de pesquisa emergente, cujo objetivo principal é extrair padrões e regras implícitos em banco de dados. Muitos algoritmos para mineração de regras de associação foram propostos. Entretanto, a pesquisa tem dado atenção principalmente à algoritmos seqüenciais. Neste artigo apresentamos a paralelização de um algoritmo para determinação de regras de associação, utilizando o paradigma de memória compartilhada. Os resultados indicam que a nossa paralelização é escalável até oito processadores, independentemente das características da massa de dados.
Referências
R. Agrawal, T. Imielinski, and A.Swami. Database mining: A performance perspective. In IEEE Trans. on Knowlegde and Data Engineering., 1993.
R. Agrawal, T. lmielinski, and A. Swami. Mining association rules between sets of items in Large data bases. In ACM SIGMOD Intl. Conf. Management of Data, May 1993.
R. Agrawal and J. Shafer. Parallel mining association rules: Design, implementation, and experience. Technical Report 10004, IBM Almaden Research Center, San Jose, CA, Jan 1996.
R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In The 20th International Conference on Very Large Data Bases, September 1994.
G. John. Enhancements to the Data Mining Process. PhD thesis, Stanford University, March 1997.
J. Park, M. Chen, and P. Yu. An effective hash based algorithm for mining associative rules. In ACM SIGMOD. ACM, May 1995.
A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In The 21st International Conference on Very Large Data Bases, 1995.
Sun Microsystems. The Ultra Enterprise 10000 Server, 1997. Technical White Paper.
M. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared-memory multi-processors. Technical Report 618, The University of Rochester Computer Science Department, Rochester, NY, 1996.
R. Agrawal, T. lmielinski, and A. Swami. Mining association rules between sets of items in Large data bases. In ACM SIGMOD Intl. Conf. Management of Data, May 1993.
R. Agrawal and J. Shafer. Parallel mining association rules: Design, implementation, and experience. Technical Report 10004, IBM Almaden Research Center, San Jose, CA, Jan 1996.
R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In The 20th International Conference on Very Large Data Bases, September 1994.
G. John. Enhancements to the Data Mining Process. PhD thesis, Stanford University, March 1997.
J. Park, M. Chen, and P. Yu. An effective hash based algorithm for mining associative rules. In ACM SIGMOD. ACM, May 1995.
A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In The 21st International Conference on Very Large Data Bases, 1995.
Sun Microsystems. The Ultra Enterprise 10000 Server, 1997. Technical White Paper.
M. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared-memory multi-processors. Technical Report 618, The University of Rochester Computer Science Department, Rochester, NY, 1996.
Publicado
28/09/1998
Como Citar
PÔSSAS, B.; PELIGRINELLI, F.; MEIRA JR., W.; CARVALHO, M.; RESENDE, R..
Paralelização de Geração de Regras de Associação. In: INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD), 10. , 1998, Búzios/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
1998
.
p. 203-212.
DOI: https://doi.org/10.5753/sbac-pad.1998.22673.
