scholarly journals Grouping Districts/Cities in Kalimantan Island Based on The People's Welfare Indicators Using Fuzzy C-Means and Subtractive Fuzzy C-Means Methods

2021 ◽  
Vol 18 (1) ◽  
pp. 141-149
Author(s):  
Nur Annisa Fitri ◽  
Memi Nor Hayati ◽  
Rito Goejantoro
Keyword(s):  
Cluster Analysis ◽  
Welfare Indicators ◽  
Fuzzy C Means ◽  
Welfare Indicator ◽  
Grouping Method ◽  
Indicator Data ◽  
Optimal Cluster

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.

Hybrid chemical reaction based metaheuristic with fuzzy c-means algorithm for optimal cluster analysis

2017 ◽  
Vol 79 ◽  
pp. 282-295 ◽  
Author(s):  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Himansu Sekhar Behera ◽  
Ajith Abraham
Keyword(s):  
Cluster Analysis ◽  
Chemical Reaction ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

Cluster Analysis with Various Algorithms for Mixed Data

2017 ◽  
pp. 282-317
Author(s):  
Abha Sharma ◽  
R. S. Thakur
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithms ◽  
Complex Problem ◽  
Experimental Results ◽  
Mixed Data ◽  
Data Set ◽  
Fuzzy C Means ◽  
Conversion Method ◽  
Inherent Structure ◽  
Numeric Data

Analyzing clustering of mixed data set is a complex problem. Very useful clustering algorithms like k-means, fuzzy c-means, hierarchical methods etc. developed to extract hidden groups from numeric data. In this paper, the mixed data is converted into pure numeric with a conversion method, the various algorithm of numeric data has been applied on various well known mixed datasets, to exploit the inherent structure of the mixed data. Experimental results shows how smoothly the mixed data is giving better results on universally applicable clustering algorithms for numeric data.

scholarly journals A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 554 ◽  
Author(s):  
Barbara Cardone ◽  
Ferdinando Di Martino
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Fuzzy Entropy ◽  
Entropy Function ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Random Initialization ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.

scholarly journals Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e029594 ◽  
Author(s):  
Concepción Violán ◽  
Quintí Foguet-Boreu ◽  
Sergio Fernández-Bertolín ◽  
Marina Guisado-Clavero ◽  
Margarita Cabrera-Bean ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Electronic Health Records ◽  
Cross Sectional Study ◽  
Secondary Outcome ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Soft Clustering ◽  
Fuzzy C Means ◽  
Electronic Health

ObjectivesThe aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.DesignA cross-sectional study was conducted based on data from electronic health records.Setting284 primary healthcare centres in Catalonia, Spain (2012).Participants916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measuresWe extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.ResultsMultimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.ConclusionsMultimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.

The application of fuzzy c-means cluster analysis and non-linear mapping to geochemical datasets: examples from Portugal

1988 ◽  
Vol 3 (2) ◽  
pp. 213-224 ◽  
Author(s):  
S.P. Vriend ◽  
P.F.M. van Gaans ◽  
J. Middelburg ◽  
A. de Nijs
Keyword(s):  
Cluster Analysis ◽  
Linear Mapping ◽  
Fuzzy C Means ◽  
Non Linear

scholarly journals Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

2019 ◽  
Vol 9 (4) ◽  
pp. 2760
Author(s):  
K. Varada Rajkumar ◽  
Adimulam Yesubabu ◽  
K. Subrahmanyam
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Fuzzy C Means ◽  
A Value ◽  
Data Points ◽  
Partition Clustering

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value &lt; 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.

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