An Improved Data Mining Method Applied to the Search of Relationship between Metabolic Syndrome and Lifestyles
A data cutting and sorting method (DCSM) is proposed
to optimize the performance of data mining. DCSM reduces the
calculation time by getting rid of redundant data during the data
mining process. In addition, DCSM minimizes the computational units
by splitting the database and by sorting data with support counts. In the
process of searching for the relationship between metabolic syndrome
and lifestyles with the health examination database of an electronics
manufacturing company, DCSM demonstrates higher search
efficiency than the traditional Apriori algorithm in tests with different
support counts.
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in Proceedings of 20th International Conference on Very Large Data
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[2] W. J. Frawley, G. Piatetsky-Shapiro, C. J. Matheus, "Knowledge
discovery databases: An overview," AI Magazine. vol. 13, no. 3, pp.
57-70, 1992.
[3] F. H. Grupe,; M. M. Owrang, " data base mining discovering new
knowledge and competitive advantage," Information systems
management, vol. 12, no. 4, pp. 26-31, 1995.
[4] M.H. Smith, and W. Pedrycz, "Expanding the meaning of and
applications for data mining," International Conference on Systems,
Man, and Cybernetics, vol. 3, 2000, pp. 1874.
[5] J. S. Park, M. Chen and P. S. Yu, "An effective hash-based algorithm for
mining association rules," in Proceedings of the 1995 ACM SIGMOD
international conference on Management of data.
[6] U. Takeaki, A. Taisuya , U. Yuzo, A. Hiroki " LCM´╝ÜAN Efficient
Algorithm for Enumerating Frequent Closed Item Sets," In Proc. IEEE
ICDM99 Workshop FIMI- 03,2003.
[7] K. M. Yu, J. Zhou, T. P. Hong, J. L. Zhou, "A Load-Balanced Distributed
Parallel Mining Algorithm," Expert Systems with Applications, vol. 37,
issue 3, pp.2459-2464, 2010.
[1] R. Agrawal, R. Srikant, "Fast Algorithms for Mining Association Rule,"
in Proceedings of 20th International Conference on Very Large Data
Bases, pp. 487-499, 1994.
[2] W. J. Frawley, G. Piatetsky-Shapiro, C. J. Matheus, "Knowledge
discovery databases: An overview," AI Magazine. vol. 13, no. 3, pp.
57-70, 1992.
[3] F. H. Grupe,; M. M. Owrang, " data base mining discovering new
knowledge and competitive advantage," Information systems
management, vol. 12, no. 4, pp. 26-31, 1995.
[4] M.H. Smith, and W. Pedrycz, "Expanding the meaning of and
applications for data mining," International Conference on Systems,
Man, and Cybernetics, vol. 3, 2000, pp. 1874.
[5] J. S. Park, M. Chen and P. S. Yu, "An effective hash-based algorithm for
mining association rules," in Proceedings of the 1995 ACM SIGMOD
international conference on Management of data.
[6] U. Takeaki, A. Taisuya , U. Yuzo, A. Hiroki " LCM´╝ÜAN Efficient
Algorithm for Enumerating Frequent Closed Item Sets," In Proc. IEEE
ICDM99 Workshop FIMI- 03,2003.
[7] K. M. Yu, J. Zhou, T. P. Hong, J. L. Zhou, "A Load-Balanced Distributed
Parallel Mining Algorithm," Expert Systems with Applications, vol. 37,
issue 3, pp.2459-2464, 2010.
@article{"International Journal of Medical, Medicine and Health Sciences:49772", author = "Yi Chao Huang and Yu Ling Liao and Chiu Shuang Lin", title = "An Improved Data Mining Method Applied to the Search of Relationship between Metabolic Syndrome and Lifestyles", abstract = "A data cutting and sorting method (DCSM) is proposed
to optimize the performance of data mining. DCSM reduces the
calculation time by getting rid of redundant data during the data
mining process. In addition, DCSM minimizes the computational units
by splitting the database and by sorting data with support counts. In the
process of searching for the relationship between metabolic syndrome
and lifestyles with the health examination database of an electronics
manufacturing company, DCSM demonstrates higher search
efficiency than the traditional Apriori algorithm in tests with different
support counts.", keywords = "Data mining, Data cutting and sorting method,
Apriori algorithm, Metabolic syndrome", volume = "5", number = "12", pages = "644-4", }