Discretizing Numerical Values by a Fuzzy Clustering Technique

2017 ◽  
Author(s):  
Andres Bueno-Crespo ◽  
Raquel Martinez-Espana ◽  
Isabel Timon ◽  
Jesus Soto
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Technique

scholarly journals Analysis of regional development disparities in Ukraine with fuzzy clustering technique

2019 ◽  
Vol 65 ◽  
pp. 04008
Author(s):  
Kateryna Gorbatiuk ◽  
Olha Mantalyuk ◽  
Oksana Proskurovych ◽  
Oleksandr Valkov
Keyword(s):  
Fuzzy Clustering ◽  
Maximum Growth ◽  
Data Sets ◽  
Economic Policies ◽  
Clustering Methods ◽  
Economic Activities ◽  
Development Indicators ◽  
Clustering Technique ◽  
Regional Cluster ◽  
Partition Clustering

Disparities in the development of regions in any country affect the entire national economy. Detecting the disparities can help formulate the proper economic policies for each region by taking action against the factors that slow down the economic growth. This study was conducted with the aim of applying clustering methods to analyse regional disparities based on the economic development indicators of the regions of Ukraine. There were considered fuzzy clustering methods, which generalize partition clustering methods by allowing objects to be partially classified into more than one cluster. Fuzzy clustering technique was applied using R packages to the data sets with the statistic indicators concerned to the economic activities in all administrative regions of Ukraine in 2017. Sets of development indicators for different sectors of economic activity, such as industry, agriculture, construction and services, were reviewed and analysed. The study showed that the regional cluster classification results strongly depend on the input development indicators and the clustering technique used for this purpose. Consideration of different partitions into fuzzy clusters opens up new opportunities in developing recommendations on how to differentiate economic policies in order to achieve maximum growth for the regions and the entire country.

Entropy Weighting Based Centralized Multi-View Fuzzy Clustering: A Case Study on Brain MR Image Segmentation

2020 ◽  
Vol 10 (7) ◽  
pp. 1654-1659
Author(s):  
Hengfei Wu ◽  
Guanglei Sheng ◽  
Lin Li
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Medical Image Segmentation ◽  
Mr Image ◽  
Clustering Technique ◽  
Fuzzy Clustering Analysis ◽  
Mr Image Segmentation ◽  
Weight Learning

Multi-view fuzzy clustering analysis is often used for medical image segmentation such as brain MR image segmentation. However, in traditional multi-view clustering, it assumes that each view plays the same role to the final partition result, which omits the negative influences caused by noisy or weak views. In this paper, a novel entropy weighting based centralized clustering technique is proposed for multi-view datasets where the Shannon entropy is hired for view weight learning. Moreover, the centralized strategy is employed for collaborate learning. Extensive experiments show that the promising performance of our proposed clustering technique. More importantly, a case study on brain MR image segmentation indicates the application ability of our clustering technique.

scholarly journals Generalized Net Model of an Intuitionistic Fuzzy Clustering Technique for Biomedical Data

2012 ◽  
Vol 26 (5) ◽  
pp. 3306-3309 ◽  
Author(s):  
Parvathi Rangasamy ◽  
Stefan Hadjitodorov ◽  
Krasimir Atanassov ◽  
Peter Vassilev
Keyword(s):  
Fuzzy Clustering ◽  
Biomedical Data ◽  
Clustering Technique ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Clustering ◽  
Generalized Net

scholarly journals Application of genetic and K-means algorithms in clustering Babakoohi Anticline joints north of Shiraz, Iran

2020 ◽  
Author(s):  
Hajar Kazemi ◽  
Kouros Yazdjerdi ◽  
Abdolmajid Asadi ◽  
Mohammad Reza Mozafari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Statistical Techniques ◽  
Clustering Technique ◽  
Primary Cluster

AbstractThe fuzzy clustering technique is one of the ways of organizing data that presents special patterns using algorithms and based on the similarity level of data. In this study, in order to cluster the resulting data from the Babakoohi Anticline joints, located north of Shiraz, K-means and genetic algorithms are applied. The K-means algorithm is one of the clustering algorithms easily implemented and of fast performance; however, sometimes this algorithm is located in the local optimal trap and cannot respond with an optimal answer, due to the sensitivity of this algorithm to the centers of the primary cluster. In addition, it has some basic disadvantages, such as its inappropriateness for complicated forms and also the dependency of the final result upon the primary cluster. Therefore, in order to perform the study more accurately and to obtain more reliable results, the genetic algorithm is used for categorizing the data of joints of the studied area. Applying this algorithm for leaving the local optimal points is an effective way. The results of clustering of the aforementioned data using the two above techniques represent two clusters in the Babakoohi Anticline. Furthermore, for validity and surveying of the results of the suggested techniques, various mathematical and statistical techniques, including ICC, Vw, VMPC, and VPMBF, are applied, which supports the similarity of the obtained results and the data clustering process in two algorithms.

RBF CENTERS INITIALIZATION USING FUZZY CLUSTERING TECHNIQUE FOR FUNCTION APPROXIMATION PROBLEMS

2005 ◽  
Vol 10 (02) ◽  
Author(s):  
A. Guillén ◽  
I. Rojas ◽  
J. González ◽  
H. Pomares ◽  
L. J. Herrera
Keyword(s):  
Fuzzy Clustering ◽  
Function Approximation ◽  
Clustering Technique ◽  
Approximation Problems
Sign in / Sign up

Export Citation Format

Share Document