Prediction of Missing Associations Using Rough Computing and Bayesian Classification

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D. P. Acharjya 1,* Debasrita Roy 1 Md. A. Rahaman 1

1. School of Computing Science & Engineering, VIT University, Vellore, TamilNadu, India

* Corresponding author.


Received: 10 Feb. 2012 / Revised: 15 Jun. 2012 / Accepted: 3 Aug. 2012 / Published: 8 Oct. 2012

Index Terms

Rough Set, Order Relation, Almost Indiscernibility, Fuzzy Proximity Relation, Missing Data, Bayesian Classification


Information technology revolution has brought a radical change in the way data are collected or generated for ease of decision making. It is generally observed that the data has not been consistently collected. The huge amount of data has no relevance unless it provides certain useful information. Only by unlocking the hidden data we can not use it to gain insight into customers, markets, and even to setup a new business. Therefore, the absence of associations in the attribute values may have information to predict the decision for our own business or to setup a new business. Based on decision theory, in the past many mathematical models such as naïve Bayes structure, human composed network structure, Bayesian network modeling etc. were developed. But, many such models have failed to include important aspects of classification. Therefore, an effort has been made to process inconsistencies in data being considered by Pawlak with the introduction of rough set theory. In this paper, we use two processes such as pre process and post process to predict the output values for the missing associations in the attribute values. In pre process we use rough computing, whereas in post process we use Bayesian classification to explore the output value for the missing associations and to get better knowledge affecting the decision making.

Cite This Paper

D. P. Acharjya, Debasrita Roy, Md. A. Rahaman, "Prediction of Missing Associations Using Rough Computing and Bayesian Classification", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.11, pp.1-13, 2012. DOI:10.5815/ijisa.2012.11.01


[1]Fayaad U M, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery: An overview. In: Advances in Knowledge Discovery and Data Mining (Fayaad U M, Piatetsky-Shapiro G, Smyth P, Uthurusamy R. Eds), American Association for Artificial Intelligence (AAAI) Press, Calfornia, 1996. 1~34.

[2]Rocha L M. TalkMine: A soft computing approach to adaptive knowledge recommendation. In: Soft Computing Agents: New Trends for Designing Autonomous Systems (V Loia, S Sessa Eds). Series on Studies in Fuziness and Soft Computing, Physica-Verlag, Springer, 2001. 89~116.

[3]Zadeh L A. Fuzzy sets. Information and Control, 1965, 8: 338-353.

[4]Pawlak Z. Rough sets. International Journal of Computer and Information Sciences, 1982, 11: 341-356. 

[5]Pawlak Z, Skowron A. Rudiments of rough sets. Information Sciences, Elsevier, 2007, 177 (1): 3-27.

[6]Pawlak Z, Skowron A. Rough sets: some extensions. Information Sciences, Elsevier, 2007, 177 (1): 28-40.

[7]Pawlak Z, Skowron A. Rough sets and Boolean reasoning. Information Sciences, Elsevier, 2007, 177 (1): 41-73.

[8]Dubois D, Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General System, 1990, 17: 191-209.

[9]Acharjya D P, Tripathy B K. Rough sets on fuzzy approximation spaces and applications to distributed knowledge systems. International Journal of Artificial Intelligence and Soft Computing, 2008, 1 (1): 1-14.

[10]Acharjya D P, Tripathy B K. Rough sets on intuitionistic fuzzy approximation spaces and knowledge representation. International Journal of Artificial Intelligence and Computational Research, 2009, 1 (1): 29-36.

[11]Dong Ya Li, Bao Qing Hu. A kind of dynamic rough sets. Proceedings of the fourth International Conference on Fuzzy Systems and Knowledge Discovery, 2007. 79~85.

[12]Slowinski R, Vanderpooten D. A generalized definition of rough approximations based on similarity. IEEE Trans. on Knowledge and Data Engineering, 2000, 12 (2): 331-336.

[13]Rubin D B. Inference and missing data. Biometrika, 1976, 63: 581-592.

[14]Little R J A, Rubin D B. Statistical analysis with missing data, Second Edition, Wiley-Interscience, NJ, USA, 2002.

[15]Yao Y Y. Information tables with neighborhood semantics. In: Data Mining and Knowledge Discovery-Theory, Tools, and Technology (Dasarathy B V. Ed.), Society for Optical Engineering, Bellingham, Washington, 2000, 2: 108~116.

[16]Tripathy B K, Acharjya D P, Cynthya V. A framework for intelligent medical diagnosis using rough set with formal concept analysis. International Journal of Artificial Intelligence and Applications, 2011, 2 (2): 45-66.

[17]Acharjya D P, Ezhilarsi L. A knowledge mining model for ranking institutions using rough computing with ordering rules and formal concept analysis. International Journal of Computer Science Issues, 2011, 8 (2): 417-425.

[18]Acharjya D P. Comparative study of rough sets on fuzzy approximation spaces and intuitionistic fuzzy approximation spaces. International Journal of Computational and Applied Mathematics, 2009, 4 (2): 95-106.

[19]Yao Y Y, Sai Ying. Mining ordering rules using rough set theory. Bulletin of International Rough Set Society, 2001, 5: 99-106.

[20]Han Jiawei, Kamber Micheline. Data Mining and Concepts and Techniques. Elsevier, New York, 2006.

[21]Lin J H, Haug P J. Exploiting missing clinical data in Bayesian network modeling for predicting medical problems. Journal of Biomedical Informatics, Elsevier, New York, 2008, 41: 1-14.

[22]Mitchell Tom M. Machine learning. McGraw Hill, New York, 1997.