Academic Journal of Computing & Information Science, 2022, 5(14); doi: 10.25236/AJCIS.2022.051402.
Zhizhong Long, Chenggong Lv
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo City, China
The semi-monolayer covering rough set has the characteristics of high approximation quality and efficient computation. In the face of massive data changes, the study of rough sets will face the challenge of high complexity computation. To address the large-scale changes of object sets in set-valued decision information systems, this paper proposes a dynamic update method based on Spark framework to solve the approximation set problem of semi-monolayer covering rough set. This method is mainly embodied in four aspects: firstly, for the change of information grains in the proposed single-layer covering rough set, this paper proposes an update strategy for the change of information units; then for the update of reliable elements, an update strategy is proposed for 〖▁C_"GC0" 〗^' (X) and 〖¯C_"GC0" 〗^' (X); secondly, for the change of reliable elements and disputed elements, an RSM matrix update strategy; and finally the update strategy of the approximation set is proposed for the changes of information units. Since the incremental methods using other models will yield different approximation sets and the prerequisite of the comparison experiments requires the approximation sets to be consistent, in order to ensure the correctness of the experimental results, this paper designs the comparison experiments of static and dynamic algorithms based on the semi-monolayer covering rough set model. The experimental results with several data sets show that the incremental algorithm speeds up in 3.67~7.72 times than the static algorithm when there is an object set update.
semi-monolayer covering rough set; parallel computation; approximation rules; set value information System; grain calculation
Zhizhong Long, Chenggong Lv. Parallel incremental update of semi-monolayer covering rough set based on object set changes. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 14: 11-21. https://doi.org/10.25236/AJCIS.2022.051402.
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