DOI QR코드

DOI QR Code

Gene Algorithm of Crowd System of Data Mining

  • Park, Jong-Min (Department of Cyber Security, Chosun College of Science & Technology)
  • Received : 2012.01.13
  • Accepted : 2012.02.27
  • Published : 2012.03.31

Abstract

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

Keywords

References

  1. M. J. A. Berry and G. Linoff, Data mining techniques: for marketing, sales, and customer support, New York: John Wiley & Sons; 1997.
  2. R. Agrawal and J. C. Shafer, "Parallel mining of association rules," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 962-969, 1996. https://doi.org/10.1109/69.553164
  3. J. Katz, R. Ostrovsky, and M. Yung, "Efficient password authenticated key exchange using human memorable passwords," Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques: Advances in Cryptology, Innsbruck, pp. 475-494, 2001.
  4. D. F. Specht, "Probabilistic neural networks," Neural Networks, vol. 3, no. 1, pp. 109-118, 1990. https://doi.org/10.1016/0893-6080(90)90049-Q
  5. M. J. L. Orr, Introduction to radial basis function networks, Edinburgh: University of Edinburgh; 1996.
  6. A. Hinneburg and D. A. Keim, "An efficient approach to clustering in large multimedia databases with noise," Proceeding of 4th International Conference of knowledge Discovery and Data Mining, NewYork, pp. 58-65, 1998.
  7. J. D. Kelly and L. Davis, "Hybridizing the genetic algorithm and the k nearest neighbors classification algorithm," Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, pp. 377-383, 1991.
  8. M. Ankerst, M. M. Breuning, H. P. Kriegel, and J. Sander, "OPTICS: ordering points to identify the clustering structure," Proceedings of ACM SIGMOD International Conference on Management of Data, Philadelphia, pp. 49-60, 1999.
  9. J. Bala, J. Huang, H. Vafaie, K. DeJong and H. Wechsler, "Hybrid learning using genetic algorithms and decision tree for pattern classification," Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, pp. 719-724, 1995.
  10. H. H. Lee, J. M. Park, and B. J. Cho, "Application of gene algorithm for the development of efficient clustering system," The International Conference on Multimedia Technology and its Applications, Uttar Pradesh,pp. 96-99, 2003.
  11. S. S. Anand, W. R. D. Patterson, J. G. Hughes, and D. A. Bell, "Discovering case knowledge using data mining," Proceedings of the 2nd Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining, Melbourne, pp. 25-35, 1998.

Cited by

  1. The Sensitivity Analysis for Customer Feedback on Social Media vol.19, pp.4, 2015, https://doi.org/10.6109/jkiice.2015.19.4.780