Granular Directed Rough Sets, Concept Organization and Soft Clustering

TL;DR

This paper extends up-directed rough sets with granular and algebraic semantics, proposing new approximation operators and rough clustering techniques relevant to distributed cognition and learning contexts.

Contribution

It introduces two new granular directions for up-directed rough sets, with algebraic semantics and approximation operators, and proposes rough clustering methods for datasets with up-directed relations.

Findings

Approximation operators satisfy cautious monotony.

Pi-groupoidal approximations have improved properties.

Proposed rough clustering techniques applicable to Sentinel project data.

Abstract

Up-directed rough sets are introduced and studied by the present author in earlier papers. This is extended by her in two different granular directions in this research, with a surprising algebraic semantics. The granules are based on ideas of generalized closure under up-directedness that may be read as a form of weak consequence. This yields approximation operators that satisfy cautious monotony, while pi-groupoidal approximations (that additionally involve strategic choice and algebraic operators) have nicer properties. The study is primarily motivated by possible structure of concepts in distributed cognition perspectives, real or virtual classroom learning contexts, and student-centric teaching. Rough clustering techniques for datasets that involve up-directed relations (as in the study of Sentinel project image data) are additionally proposed. This research is expected to see significant theoretical and practical applications in related domains.