Rough Set Theory Applied to Simple Undirected Graphs

Chiaselotti, Giampiero; Ciucci, Davide; Gentile, Tommaso; Infusino, Federico

doi:10.1007/978-3-319-25754-9_37

Cited by 11 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, we do not discuss rough lower and upper approximation functions, rough membership function and attribute dependency. For a detailed treatment concerning these notions in undirected graph theory we refer the reader to [8,9,11].…”

Section: An Example Of Dm → Grc-rsmentioning

confidence: 99%

See 1 more Smart Citation

Simple graphs in granular computing

Chiaselotti

Ciucci

Gentile

2016

Information Sciences

Self Cite

View full text Add to dashboard Cite

Section: An Example Of Dm → Grc-rsmentioning

confidence: 99%

“…Specific links between graph theory and rough set theory have been investigated in [4,24,45,44,9,10]. In [4] rough sets are used to test bipartiteness of simple undirected graphs.…”

Section: Related Workmentioning

confidence: 99%

Simple graphs in granular computing

Chiaselotti

Ciucci

Gentile

2016

Information Sciences

Self Cite

View full text Add to dashboard Cite

“…The graph theory is currently used in granular computing and the representation of seed structures for computer problems research. For example, the adjacency matrix of a simple undirected graph (constrained) is interpreted as a Boolean information table (22).…”

Section: Introductionmentioning

confidence: 99%

New Approach Based on Fuzzy Hypergraphs in Granular Computing (An Application to the Urban Vulnerability Assessment)

Baghini

Babaei

Mirhosseini

2022

Preprint

View full text Add to dashboard Cite

Classifying objects based on the simultaneous impact of various parameters has always been challenging due to heterogeneity, impact conflict, and sometimes parameter uncertainty. The purpose of this study is to provide a method for classifying such data. In the proposed method, fuzzy hypergraphs were used to define the granular structures in order to apply the simultaneous effect of heterogeneous and weighted parameters in the classification. This method has been implemented and validated on Fisher intuitive research in relation to the classification of iris flowers. Evaluation and comparison of the proposed method with Fisher’s experimental and results showed higher efficiency and accuracy in flower classification. The proposed method has been used to assess the seismic risk of 50,000 buildings based on 10 heterogeneous parameters. Seismic risk classification showed that more than 88% of buildings were classified, and 12% of buildings that could not be classified due to excessive scatter of parameter values were classified using a very small confidence radius. The results indicate the ability of the proposed method to classify objects with the least similarity and number of effective parameters in classification.

show abstract

“…In fact, in [22,23,24,25,26] the idea to study any simple undirected graph G as if it were a Boolean information system was developed (in [16] this idea has been also extended to hypergraph theory). The basic tool to connect graphs and Boolean information systems is the adjacency matrix of G, which in [22,23,24,25,26] has been interpreted as the Boolean table of a particular information system. Specifically, in [22,23,24] some graph families, such as the complete graph K n or the complete bipartite graph K p,q , have been broadly studied in terms of information tables.…”

Section: Introductionmentioning

confidence: 99%

“…The basic tool to connect graphs and Boolean information systems is the adjacency matrix of G, which in [22,23,24,25,26] has been interpreted as the Boolean table of a particular information system. Specifically, in [22,23,24] some graph families, such as the complete graph K n or the complete bipartite graph K p,q , have been broadly studied in terms of information tables. In particular, in the above papers, the A-lower and the A-upper approximations, the A-positive region of any vertex subset B, the A-attribute dependency function and the rough membership function, where A, B and Y are vertex subsets, have been completely determined both for the complete graph K n and the complete bipartite graph K p,q .…”

Section: Introductionmentioning

confidence: 99%

Rough Sets for n-Cycles and n-Paths

Chiaselotti¹,

Gentile²,

Infusino³

et al. 2016

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

In this paper we carry out a research project whose main goal is the study of an undirected graph by means of investigation tools provided by Pawlak rough set theory. Specifically, we determine both the lower and the upper rough approximation functions for an information table induced from a cycle or a path on n vertices. For such graphs we also provide a complete description of the corresponding exact subsets.

show abstract

Rough Set Theory Applied to Simple Undirected Graphs

Cited by 11 publications

References 8 publications

Simple graphs in granular computing

Simple graphs in granular computing

New Approach Based on Fuzzy Hypergraphs in Granular Computing (An Application to the Urban Vulnerability Assessment)

Rough Sets for n-Cycles and n-Paths

Contact Info

Product

Resources

About