Grouping trips by fuzzy similarity for scheduling of demand‐responsive transportation vehicles

1994 ◽  
Vol 18 (1) ◽  
pp. 65-80 ◽  
Author(s):  
Shinya Kikuchi ◽  
Natasa Vukadinovic
Keyword(s):  
Fuzzy Similarity

Using fuzzy similarity priority ratio to analyse climate similarity between Fujian and Taiwan for fruit tree cultivation

2013 ◽  
Vol 21 (9) ◽  
pp. 1149-1156
Author(s):  
Li-Chun LI ◽  
Jia-Jin CHEN ◽  
Jing LIN ◽  
Chuan-Rong HUANG ◽  
Yun-Yun LU ◽  
...  
Keyword(s):  
Fruit Tree ◽  
Fuzzy Similarity

scholarly journals Fuzzy Covering-Based Three-Way Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dandan Yang
Keyword(s):  
Nearest Neighbor ◽  
Structural Information ◽  
Boundary Region ◽  
Global Perspective ◽  
Semantic Interpretation ◽  
Entropy Theory ◽  
Fuzzy Similarity ◽  
Fuzzy Similarity Relation ◽  
The Universe ◽  
Better Than

This paper investigates the three-way clustering involving fuzzy covering, thresholds acquisition, and boundary region processing. First of all, a valid fuzzy covering of the universe is constructed on the basis of an appropriate fuzzy similarity relation, which helps capture the structural information and the internal connections of the dataset from the global perspective. Due to the advantages of valid fuzzy covering, we explore the valid fuzzy covering instead of the raw dataset for RFCM algorithm-based three-way clustering. Subsequently, from the perspective of semantic interpretation of balancing the uncertainty changes in fuzzy sets, a method of partition thresholds acquisition combining linear and nonlinear fuzzy entropy theory is proposed. Furthermore, boundary regions in three-way clustering correspond to the abstaining decisions and generate uncertain rules. In order to improve the classification accuracy, the k-nearest neighbor (kNN) algorithm is utilized to reduce the objects in the boundary regions. The experimental results show that the performance of the proposed three-way clustering based on fuzzy covering and kNN-FRFCM algorithm is better than the compared algorithms in most cases.

GENERALIZED FUZZY ROUGH SETS BY CONDITIONAL PROBABILITY RELATIONS

2002 ◽  
Vol 16 (07) ◽  
pp. 865-881 ◽  
Author(s):  
ROLLY INTAN ◽  
MASAO MUKAIDONO
Keyword(s):  
Conditional Probability ◽  
Rough Set ◽  
Real World ◽  
Rough Sets ◽  
Similarity Relation ◽  
Fuzzy Rough Set ◽  
Fuzzy Similarity ◽  
Fuzzy Similarity Relation ◽  
Real World Applications ◽  
The Universe

In 1982, Pawlak proposed the concept of rough sets with a practical purpose of representing indiscernibility of elements or objects in the presence of information systems. Even if it is easy to analyze, the rough set theory built on a partition induced by equivalence relation may not provide a realistic view of relationships between elements in real-world applications. Here, coverings of, or nonequivalence relations on, the universe can be considered to represent a more realistic model instead of a partition in which a generalized model of rough sets was proposed. In this paper, first a weak fuzzy similarity relation is introduced as a more realistic relation in representing the relationship between two elements of data in real-world applications. Fuzzy conditional probability relation is considered as a concrete example of the weak fuzzy similarity relation. Coverings of the universe is provided by fuzzy conditional probability relations. Generalized concepts of rough approximations and rough membership functions are proposed and defined based on coverings of the universe. Such generalization is considered as a kind of fuzzy rough set. A more generalized fuzzy rough set approximation of a given fuzzy set is proposed and discussed as an alternative to provide interval-value fuzzy sets. Their properties are examined.

scholarly journals Applying Geospatial Semantic Array Programming for a Reproducible Set of Bioclimatic Indices in Europe

2014 ◽  
Author(s):  
Giovanni Caudullo
Keyword(s):  
Mathematical Formulation ◽  
Open Data ◽  
Global Scale ◽  
Ecological Niches ◽  
European Continent ◽  
Bioclimatic Indices ◽  
Temperature And Precipitation ◽  
Food And Agriculture ◽  
Fuzzy Similarity ◽  
Terrain Elevation

Bioclimate-driven regression analysis is a widely used approach for modelling ecological niches and zonation. Although the bioclimatic complexity of the European continent is high, a particular combination of 12 climatic and topographic covariates was recently found able to reliably reproduce the ecological zoning of the Food and Agriculture Organization of the United Nations (FAO) for forest resources assessment at pan-European scale, generating the first fuzzy similarity map of FAO ecozones in Europe. The reproducible procedure followed to derive this collection of bioclimatic indices is now presented. It required an integration of data-transformation modules (D-TM) using geospatial tools such as Geographic Information System (GIS) software, and array-based mathematical implementation such as semantic array programming (SemAP). Base variables, intermediate and final covariates are described and semantically defined by providing the workflow of D-TMs and the mathematical formulation following the SemAP notation. Source layers to derive base variables were extracted by exclusively relying on global-scale public open geodata in order for the same set of bioclimatic covariates to be reproducible in any region worldwide. In particular, two freely available datasets were exploited for temperature and precipitation (WorldClim) and elevation (Global Multi-resolution Terrain Elevation Data). The working extent covers the European continent to the Urals with a resolution of 30 arc-second. The proposed set of bioclimatic covariates will be made available as open data in the European Forest Data Centre (EFDAC). The forthcoming complete set of D-TM codelets will enable the 12 covariates to be easily reproduced and expanded through free software. .......................................................................................................................This.manuscript.has.been.accepted.for.publication.in IEEE Earthzine 2014 Vol. 7 Issue 2, 2ndquarter theme: Geospatial Semantic Array Programming. The definitive version has been published at: http://www.earthzine.org/?p=877975......................................................................................................................Please,.cite.the.definitive.version.of.the.article.as: Caudullo, G., 2014.Applying Geospatial Semantic Array Programming for a Reproducible Set of Bioclimatic Indices in Europe.IEEE Earthzine 7(2), 877975+. URL http://www.earthzine.org/?p=877975 bioRxiv pre-print doi: 10.1101/009589

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