Will you tell us what is the major achievement of this paper, and also what is further study issue from this? Thanks in advance!!
Major achievement of this paper includes 1) Considering preference relation of attributes, we explore proposition based incremental technique to update approximations of DRSA upon insertion of new objects, in time efficient manner. 2) A dynamic method to update approximations of DRSA upon deletion of objects which is computationally efficient. 3) Along with dynamic algorithms for maintaining approximations, experiments are carried out to evaluate the advantages of the proposed approach. The experimental results of comparison of our proposed approach with conventional DRSA algorithm and another recent incremental method, showed that our proposed technique successfully update approximations in reduce time, without compromising accuracy. This shows that our method can be used successfully for dynamic applications which based on DRSA approximations.
In this study, we explored single dimension variation in dataset and update approximations by assuming that conditional and decisional attribute set along with value set of conditional and decision attribute remained unchanged. In future, we want to enhance its functionality to handle multi-dimensional variations i.e., variation in conditional attribute set, decision attribute set and value set of conditional and decision attribute.
