Subsethood Measures of Spatial Granules
Liquan Zhao, Yiyu Yao

TL;DR
This paper introduces measures of subsethood within spatial granules, establishing axioms, and developing entropy measures to enhance the understanding of set inclusion and granularity in spatial rough set models.
Contribution
It generalizes subsethood and supsethood to conditional measures and develops new entropy measures, advancing the theoretical framework of spatial rough granules.
Findings
Developed five conditional granularity measures.
Proved axioms for each measure.
Established entropy measures satisfying monotone conditions.
Abstract
Subsethood, which is to measure the degree of set inclusion relation, is predominant in fuzzy set theory. This paper introduces some basic concepts of spatial granules, coarse-fine relation, and operations like meet, join, quotient meet and quotient join. All the atomic granules can be hierarchized by set-inclusion relation and all the granules can be hierarchized by coarse-fine relation. Viewing an information system from the micro and the macro perspectives, we can get a micro knowledge space and a micro knowledge space, from which a rough set model and a spatial rough granule model are respectively obtained. The classical rough set model is the special case of the rough set model induced from the micro knowledge space, while the spatial rough granule model will be play a pivotal role in the problem-solving of structures. We discuss twelve axioms of monotone increasing subsethood and…
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Taxonomy
TopicsRough Sets and Fuzzy Logic · Advanced Decision-Making Techniques
