Workshop on Three-Way Data Analytics (3WDA 2027)

CALL FOR PAPERS – 3WDA 2027

Three-way decision (3WD) is a theory of thinking in threes, problem solving in threes, and computing in threes. Originally formalized by Professor Yiyu Yao in the early 2010s as a decision-theoretic extension of probabilistic rough sets, 3WD has progressively evolved into a general methodology for reasoning, learning, information processing, and decision-making in complex systems.

The development of 3WD has revealed a broader methodological perspective: many real-world problems cannot be adequately characterized by binary thinking. Complex data analytics often requires distinguishing between what is sufficiently supported, what is sufficiently unsupported, and what remains uncertain — between information that can be immediately processed and information that should be deferred for further investigation. By introducing a third option, 3WD provides a principled way to represent uncertainty, fuzzy, and incomplete knowledge. In this sense, three-way thinking provides not merely a decision rule, but a general paradigm for preparing and analyzing data in threes.

As we approach IJCRS 2027, three-way decision has become increasingly connected with several important research directions in rough sets and granular computing. Recent theoretical developments and applications have extended 3WD beyond its original probabilistic interpretation toward three-world thinking, multi-level and multi-granularity information processing, dynamic and sequential decision-making, and intelligent data analytics. These developments motivate a broader exploration of three-way data analytics, in which triadic structures, three-way representations, and three-way reasoning are systematically integrated into the data analytics pipeline.

This workshop aims to provide a premier forum for researchers to investigate the emerging intersection of three-way decision, rough sets, granular computing, and data analytics. Particular attention will be devoted to how three-way thinking can contribute to the analysis of complex, uncertain, heterogeneous, dynamic, and large-scale data. The workshop welcomes both foundational theoretical studies and application-oriented research that demonstrates the usefulness of three-way approaches in contemporary data-driven systems.