A Geometric Fuzzy-Based Approach for Airport Clustering | Zendy

Maria Nadia Postorino | Zendy; Mario Versaci | Zendy

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A Geometric Fuzzy-Based Approach for Airport Clustering

Author(s) -

Maria Nadia Postorino,

Mario Versaci

Publication year - 2014

Publication title -

advances in fuzzy systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.38

H-Index - 19

eISSN - 1687-711X

pISSN - 1687-7101

DOI - 10.1155/2014/201243

Subject(s) - identification (biology) , point (geometry) , fuzzy logic , measure (data warehouse) , computer science , field (mathematics) , metric (unit) , fuzzy clustering , line (geometry) , cluster analysis , similarity (geometry) , data mining , mathematics , artificial intelligence , engineering , geometry , operations management , botany , pure mathematics , image (mathematics) , biology

Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations

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