Classification of Turkish Republics with Specific Economic Indicators in Fuzzy Clustering Analysis

International Conference on Eurasian Economies 2015 ◽

10.36880/c06.01253 ◽

2015 ◽

Author(s):

Necati Alp Erilli ◽

Çağatay Karaköy

Keyword(s):

Cluster Analysis ◽

Soviet Union ◽

Fuzzy Clustering ◽

Clustering Analysis ◽

Fuzzy Model ◽

Fuzzy Cluster ◽

Economic Indicators ◽

Important Place ◽

Fuzzy Clustering Analysis ◽

Analysis Methods

Economic indicators in economic policies have an important place in determining the levels of development. Determining and classifying the existing social and economic structures of countries is very important for examining the development states and possible development tendencies of countries and forming regional development policies in line with these. The aim in cluster analysis, is to classify datas in to similarity and perform useful knowledge for the researcher. Cluster analysis, which became more popular among the subjects of statistical classification in recent years, can give more reliable results when there is apriori knowledge about number of clusters. Fuzzy models interested in fuzzy model structures and try to estimate system behaviours that has no knowledge about their structure. Fuzzy Cluster Analysis is try to decompose the groups which membership degrees cannot be determined. When the number of datas and variables increased or cluster structures came to closer for all, Cluster analysis has given more successful results then the other cluster analysis methods. In this study, Turkish Republics were classified in terms of the indicators determined by using Fuzzy C-Means (FCM) and Gath Geva methods which are frequently used in fuzzy clustering analysis. The objective was to find out the common class structures of Turkish Republics which came out with the disintegration of the Soviet Union in 1991 and which experienced economic similar problems and thus to help countries in the same clusters in similar economic planning. Results are also compared between fuzzy and crisp clustering analysis methods.

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Fuzzy clustering analysis to study geomagnetic coastal effects

Annales Geophysicae ◽

10.5194/angeo-23-1157-2005 ◽

2005 ◽

Vol 23 (4) ◽

pp. 1157-1163 ◽

Cited By ~ 1

Author(s):

M. Sridharan ◽

N. Gururajan ◽

A. M. S. Ramasamy

Keyword(s):

Cluster Analysis ◽

Fuzzy Clustering ◽

Computer Technology ◽

Clustering Analysis ◽

East Coast ◽

Equatorial Electrojet ◽

Analytical Technique ◽

Fuzzy Clustering Analysis ◽

The Subject

Abstract. The utility of fuzzy set theory in cluster analysis and pattern recognition has been evolving since the mid 1960s, in conjunction with the emergence and evolution of computer technology. The classification of objects into categories is the subject of cluster analysis. The aim of this paper is to employ Fuzzy-clustering technique to examine the interrelationship of geomagnetic coastal and other effects at Indian observatories. Data from the observatories used for the present studies are from Alibag on the West Coast, Visakhapatnam and Pondicherry on the East Coast, Hyderabad and Nagpur as central inland stations which are located far from either of the coasts; all the above stations are free from the influence of the daytime equatorial electrojet. It has been found that Alibag and Pondicherry Observatories form a separate cluster showing anomalous variations in the vertical (Z)-component. H- and D-components form different clusters. The results are compared with the graphical method. Analytical technique and the results of Fuzzy-clustering analysis are discussed here.

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Min-Max Partition Method of Product Modularization Based on Fuzzy Clustering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.308-310.273 ◽

2011 ◽

Vol 308-310 ◽

pp. 273-279

Author(s):

Yan Hui Chen ◽

De Jian Zhou

Keyword(s):

Fuzzy Clustering ◽

Clustering Analysis ◽

Fuzzy Cluster ◽

Fuzzy Cluster Analysis ◽

Fuzzy Clustering Analysis ◽

Computing Unit ◽

Initial Partition ◽

Wheel Loaders ◽

Partition Method ◽

Module Partition

This paper presents a new method of product module partition based on the fuzzy clustering analysis. This method demonstrates the relevant definitions and calculation methods of the initial partition, min-max partition, submodule relevancy and module aggregation etc., and establishes the incidence matrix to respectively carry out the initial partition for the products and calculation of min-max partition according to various incidence relations between parts and components. Taking the submodule as computing unit in each module set, this paper carries out the fuzzy cluster analysis to obtain the module partition results of the products, and finally demonstrates the rationality and effectiveness of this method by taking the example of the working units of the wheel loaders.

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The Research of Data Mining Based on Fuzzy Cluster

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.88-89.763 ◽

2011 ◽

Vol 88-89 ◽

pp. 763-766

Author(s):

Fu Gui Fang

Keyword(s):

Fuzzy Clustering ◽

Fuzzy Set ◽

Clustering Analysis ◽

Fuzzy Relation ◽

Fuzzy Cluster ◽

Basic Knowledge ◽

Analysis Method ◽

Fuzzy Clustering Analysis ◽

Definition Of ◽

Important Branch

Fuzzy clustering analysis is an important branch of unsupervised pattern recognition. Studying the algorithm and applications of fuzzy clustering is of great significance. This paper introduces the basic knowledge of fuzzy set theory, including the definition of the fuzzy set, its theorem fuzzy relation and so on firstly. Then this paper describes how to use fuzzy clustering analysis method for data classification and the steps of fuzzy clustering analysis.

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