Rule generation in Rough set Non-deterministic Information Analysis (RNIA) and some applications of the obtained rules

We submitted this paper to the special issue, “Four Decades of Rough Set Theory: Achievements and Future.” We have researched a theory and an execution tool for rule generation from a Deterministic Information System (DIS) and a Non-deterministic Information System (NIS). We developed the NIS-Apriori algorithm, which combines the rough sets-based concept and the Apriori algorithm, for rule generation from NIS. We term this research series as a Rough set Non-deterministic Information Analysis (RNIA). In the first half of this paper, we describe the framework of RNIA and its execution environment, as well as the results we achieved. Then, later in this paper, we enumerate various applications of RNIA, such as detection of data dependencies, decision support, estimation and completion of missing values, the problem of learning DIS from NIS, and generation of rules from non-tabular and multiple heterogeneous data sets. They are our current and prospective subjects. RNIA’s capabilities can lead to several developments. • Our past research on RNIA is reviewed with its execution videos. • Attribute dependency (or feature selection) analysis using the obtained rules is studied. • Solutions to Missing value imputation and Machine Learning by Rule Generation (MLRG) are presented with their execution videos. • A Descriptor-based Information System (DbIS) for rule generation from non-tabular data sets is studied.