Author(s)

Riasat Azim¹, Abm Munibur Rahman², Shawon Barua³, Israt Jahan⁴

¹School of Computer Science & Engineering, Wuhan University of Technology, Wuhan, China.
²School of Management, Wuhan University of Technology, Wuhan, China.
³Infolytx Inc., Dhaka, Bangladesh.
⁴East West University, Dhaka, Bangladesh.

Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.

Rough Set Theory, Big Data, Risk Analysis, Data Mining, Variable Weight, Significance of Attribute, Core Attribute, Attribute Reduction

Azim, R. , Rahman, A. , Barua, S. and Jahan, I. (2016) Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach. Journal of Data Analysis and Information Processing, 4, 101-114. doi: 10.4236/jdaip.2016.43009.