2020 Volume 32 Issue 4 Pages 747-758
In this paper, information incompleteness in tabular data and rule generation in tabular data are focused on, and the previously proposed important researches are surveyed from the viewpoint of rough sets and data mining. Then, “Rough sets Non-deterministic Information Analysis (RNIA)” and “a NIS-Apriori system” proposed by the authors are described. The framework of rule generation from tables with information incompleteness is also outlined. RNIA offers a new framework of rough sets based on possible world semantics, and it gives one solution algorithm for the rule generation problem considering information incompleteness. This solution algorithm is termed “NIS-Apriori” and implemented as a NIS-Apriori system in SQL language. We also mention the new possibility of rough sets non-deterministic information analysis that employs the NIS-Apriori algorithm as the core algorithm.
