Advanced SearchSearch Tips
Creation of Approximate Rules based on Posterior Probability
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Creation of Approximate Rules based on Posterior Probability
Park, In-Kyu; Choi, Gyoo-Seok;
  PDF(new window)
In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.
Data Mining;Cluster Analysis;Uncertainty;Entropy;Rough Set;
 Cited by
Williams, Grahm J. and Simoff, Simeon J. "Data Mining Theory, Methodology, Techniques and Applications(Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence)", Springer, 2007

Ramakrishnan., Naren and Grama, Ananth Y,, "Data Mining: From Serendipity to Science", IEEE Computer August Vol. 34-37, 1999

Han, Jiawei, Kamber, Micheline, "Data Mining: Concepts and Techniques", San Franciso CA, USA, Morgan, Kaufmann, Publishers, 2001.

Hand, D.J., Mannila, H., & Smyth, P. "Principles of Data Mining", Cambridge, MA:MIT Press, 2001

Beaubouef, T., Petry, F. E. and Arora, G., Information-theoretic measures of uncertainty for rough sets and rough relational databases, Information Science, Vol. 109, No. 1-4, pp. 185-195, 1998. crossref(new window)

Pawlak, Z., "Rough sets", International Journal of Information Sciences, 11, pp. 341-356, 1982 crossref(new window)

Pawlak, Z., "Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression", Jouranl of Integrative Bioinformatics, Vol. 9, No. 3, pp.1-17, 2012

Pal S.K., Skowron, "Rough Fuzzy Hybridization: A new trend in decision making", Springer Verlag, Berlin, 1999

R. Vashist, M.L. Garg, "Rule Generation based on Reduct and Core: A Rough Set Approach", International Journal of Computer Applications, Vol. 29, No. 9, pp. 0975-8887, Sept. 2011

Lin S., Jiucheng X., Zhan'ao X. and Lingjun Z., "Rough Entropy-based Feature Selection and Its Application", Journal of International Computational Science, Vol. 8, No. 9, pp. 1525-1532, 2011

Inkyoo P., "The generation of Control Rules for Data Mining", The Journal of Digital Policy and Management, Vol. 11, No. 11, pp. 343-349, 2013