Md. A. Rahaman

Work place: School of Computing Science & Engineering, VIT University, Vellore, TamilNadu, India



Research Interests: Autonomic Computing, Computing Platform, Data Structures and Algorithms


Md. A Rahaman received his B. Tech. degree in computer science from West Bengal University of Technology, West Bengal, India in 2011. He is a M. Tech (CSE) final year student of VIT University, Vellore, India. He has keen interest in teaching and applied research. He has published two papers in International Conference. His research interest includes rough computing, and soft computing.

Author Articles
Prediction of Missing Associations Using Rough Computing and Bayesian Classification

By D. P. Acharjya Debasrita Roy Md. A. Rahaman

DOI:, Pub. Date: 8 Oct. 2012

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.

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