RBF CENTERS INITIALIZATION USING FUZZY CLUSTERING TECHNIQUE FOR FUNCTION APPROXIMATION PROBLEMS

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.

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Discretizing Numerical Values by a Fuzzy Clustering Technique

2017 International Conference on Intelligent Environments (IE) ◽

10.1109/ie.2017.37 ◽

2017 ◽

Author(s):

Andres Bueno-Crespo ◽

Raquel Martinez-Espana ◽

Isabel Timon ◽

Jesus Soto

Keyword(s):

Fuzzy Clustering ◽

Clustering Technique

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

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3091 ◽

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.

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GENERATING BALANCED LEARNING AND TEST SETS FOR FUNCTION APPROXIMATION PROBLEMS

International Journal of Neural Systems ◽

10.1142/s0129065711002791 ◽

2011 ◽

Vol 21 (03) ◽

pp. 247-263 ◽

Cited By ~ 7

Author(s):

J. P. FLORIDO ◽

H. POMARES ◽

I. ROJAS

Keyword(s):

Function Approximation ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Original Data ◽

Input Output ◽

Output Data ◽

Data Set ◽

Out Of Sample ◽

Test Sets ◽

Approximation Problems

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learning's accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

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