Evaluation of Role Mining Results Using Data Centric Approach

While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. However the two core problems in role mining such as role minimization and edge concentration are Nondeterministic polynomial. Trial and error approach is use to determine the role mining algorithm but it is time consuming due to computational overhead in mining. large data set. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms by adopting a data centric approach that quickly estimates the role mining results without running any role mining algorithm. Our approach illustrated to obtain the result which is accurate effective for role based assignments. We tackle the problem from a fresh angle. Instead of developing fast role mining algorithms, we adopt a data-centric approach that quickly estimates the bounds on optimal role mining results without actually running any role mining algorithm.