A Geometric Fuzzy-Based Approach for Airport Clustering

Abstract

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.

A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

“…e ABC-based deployment is guaranteed to extend the lifetime by optimizing the network parameters and constraining the total number of deployed relays. In [21], the authors proposed a fuzzy-based procedure to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. A regularization approach based on evolutionary computation was proposed to obtain an imagery reconstructing the corrosion profile [22].…”

A Similarity Classifier with Bonferroni Mean Operators

“…Similarity based classifiers (see [21]) have been shown to have the ability to work well on medical diagnosis problems (see, e.g., [11,22]) and have advantages such as fast speed and high classification accuracy and have already been shown to work rather well with small sets of samples (see, e.g., [20]). For more information about fuzzy classification and clustering methods, see [23][24][25][26][27][28][29].…”

“…Similarity based classifiers (see [21]) have been shown to have the ability to work well on medical diagnosis problems (see, e.g., [11,22]) and have advantages such as fast speed and high classification accuracy and have already been shown to work rather well with small sets of samples (see, e.g., [20]). For more information about fuzzy classification and clustering methods, see [23][24][25][26][27][28][29].The rest of the paper is organized as follows: in the second section we briefly go through the aggregation operators, the weight generation schemes for the new OWA based classifier variants, and the similarity measures applied in the paper, in the third section we introduce the new similarity classifiers and the new variants, and in the fourth section we first shortly introduce the used medical research data sets and then examine the achieved results. The paper is closed with discussion and conclusions.…”

Path Optimization along Buoys Based on the Shortest Path Tree with Uncertain Atmospheric and Oceanographic Data

“…Cacciola et al provided exhaustive details on how to build a fuzzy system [ 13 ]. Postorino and Versaci explained in detail how to easily structure a bank of fuzzy rules [ 14 ]. Structuring fuzzy or neuro-fuzzy rule banks was capable of modeling the problem of uncertainty and imprecision present in the data.…”