scholarly journals Classification of Countries According to their Tourism Statistics via Different Cluster Analysis Methods and the Place of Turkey in this Structure

2013 ◽  
Author(s):  
Selay Giray
Keyword(s):  
Cluster Analysis ◽  
Hong Kong ◽  
Russian Federation ◽  
Fuzzy Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Analysis Methods ◽  
Fuzzy Clustering Method ◽  
The World

The aim of this study is to classify the countries according to their tourism indicators via different cluster analysis methods and compare the findings. Using classical cluster analysis and fuzzy clustering together will be more appropriate to determine the World tourism structure. In this way the findings can be interpreted more detailed and comparatively. Data obtained from website of Worldbank (3 basic international tourism statistics of 159 countries for the year 2010) and findings are gained using NCSS (statistical software) 2007. According to the findings of fuzzy clustering method, Turkey belogs to a cluster which contains ABD, United Kingdom, China, Austria, France, Germany, Italy, Malaysia, Spain, Hong Kong, Russian Federation, and Ukraine. According to the findings of classical clustering method (k means), Turkey is in the same cluster with same countries except Hong Kong. Also the findings of two techniques are similar about Turkey. Such a result can be expected correspondingly grading the countries about international their tourism data in 2011. Different clustering methods findings are steady about Euroasian countries too. Except Russian Federation and Ukraine all of the other Euroasian countries are located together in same cluster depending upon two different clustering methods. In conclusion two different clustering methods provide consistent (similar) results about the classification of countries according their internatianol tourism statistics.

Extension-Based Clustering Method: An Approach to Support Adaptable Design of the Product

2007 ◽  
Author(s):  
Yanwei Zhao ◽  
Huijun Tang ◽  
Nan Su ◽  
Wanliang Wang
Keyword(s):  
Fuzzy Clustering ◽  
Design Method ◽  
Direct Method ◽  
Threshold Value ◽  
Minimal Distance ◽  
Clustering Methods ◽  
Clustering Method ◽  
Adaptable Design ◽  
Fuzzy Clustering Method ◽  
Distance Formula

Design for product adaptability is one of the techniques used to provide customers with products that exactly meet their requirements. Clustering methods have been used extensively in the study of product adaptability design. Of the clustering methods, the fuzzy clustering method is the most widely in the design field. The three main kinds of fuzzy clustering methods are the transitive closure method, the dynamic direct method and the maximum tree method. The dynamic direct clustering method has been found to produce design solutions with the lowest cost. In this paper, a new approach for obtaining adaptable product designs using the clustering method is proposed. The method consists of three steps. Firstly, the extension distance formula is used to determine the distance between two products in a product database. The product design space and the distances between individuals are used as grouping criteria in this step. Secondly, the minimal distance between products is used to obtain the clustering index. Thirdly, the threshold value is used to divide the products in the database into groups. Customer demands and the results obtained from the adaptable function (based on the extension distance formula) are used to evaluate the fitness of the groups and their corresponding products. The product with the largest adaptable function value to demand ratio is selected product. In order to the show the advantage of using the extension-clustering method, both the extension-clustering method and the dynamic direct method are presented and compared. The comparison indicates that the extension-clustering method leads to quicker evaluations of design alternatives and results that more closely match customers’ demands. An example of the adaptable design of circular saws tools is used to demonstrate that with the extension-clustering design method a high variety of intelligent configurations can be obtained with significant rapidity.

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

Power-Regularized Fuzzy Clustering for Spherical Data

2018 ◽  
Vol 22 (2) ◽  
pp. 163-171
Author(s):  
Yuchi Kanzawa ◽  
Keyword(s):  
Fuzzy Clustering ◽  
Numerical Experiments ◽  
Clustering Methods ◽  
Clustering Method ◽  
Spherical Data ◽  
Fuzzy Clustering Method ◽  
Fuzzy Clustering Methods ◽  
Artificial Datasets

In this paper, a power-regularization-based fuzzy clustering method is proposed for spherical data. Power regularization has not been previously applied to fuzzy clustering for spherical data. The proposed method is transformed to the conventional fuzzy clustering method, entropy-regularized fuzzy clustering for spherical data (eFCS), for a specified fuzzification parameter value. Numerical experiments on two artificial datasets reveal the properties of the proposed method. Furthermore, numerical experiments on four real datasets indicate that this method is more accurate than the conventional fuzzy clustering methods: standard fuzzy clustering for spherical data (sFCS) and eFCS.

Automatic classification of gait patterns in children with cerebral palsy using fuzzy clustering method

2020 ◽  
Vol 73 ◽  
pp. 189-194
Author(s):  
Hamed Darbandi ◽  
Mina Baniasad ◽  
Soroush Baghdadi ◽  
Aminreza Khandan ◽  
Amirreza Vafaee ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Fuzzy Clustering ◽  
Automatic Classification ◽  
Clustering Method ◽  
Gait Patterns ◽  
Fuzzy Clustering Method ◽  
Children With Cerebral Palsy

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

GT2-CFC: General Type-2 Collaborative Fuzzy Clustering method

2021 ◽  
Author(s):  
Fariba Salehi ◽  
Mohammad Reza Keyvanpour ◽  
Arash Sharifi
Keyword(s):  
Fuzzy Clustering ◽  
General Type ◽  
Clustering Method ◽  
Fuzzy Clustering Method
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