scholarly journals Multimodal Optimization using Self-Adaptive Real Coded Genetic Algorithm with K-means & Fuzzy C-means Clustering

2011 ◽  
Vol 2 (8) ◽  
Author(s):  
Vrushali K ◽  
Rahila Patel
Keyword(s):  
Genetic Algorithm ◽  
Multimodal Optimization ◽  
Fuzzy C Means ◽  
Real Coded Genetic Algorithm ◽  
Fuzzy C Means Clustering ◽  
Self Adaptive

Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm

2021 ◽  
pp. 917-927
Author(s):  
Sanat Kumar Sahu ◽  
A. K. Shrivas
Keyword(s):  
Genetic Algorithm ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Hierarchical Clustering ◽  
Adjusted Rand Index ◽  
Feature Selection Technique ◽  
Fuzzy C Means ◽  
Clustering Model ◽  
Fuzzy C Means Clustering ◽  
Evolutionary Technique

The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical clustering was the lowest at 92. 25%. After using evolutionary technique Genetic Algorithm as Feature selection technique, the best purity is obtained by hierarchical clustering, 97.50%, compared to K –means clustering, 96.75%, and Fuzzy C-means clustering at 94.00%.

Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm

2018 ◽  
Vol 8 (4) ◽  
pp. 16-25
Author(s):  
Sanat Kumar Sahu ◽  
A. K. Shrivas
Keyword(s):  
Genetic Algorithm ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Hierarchical Clustering ◽  
Adjusted Rand Index ◽  
Feature Selection Technique ◽  
Fuzzy C Means ◽  
Clustering Model ◽  
Fuzzy C Means Clustering ◽  
Evolutionary Technique

The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical clustering was the lowest at 92. 25%. After using evolutionary technique Genetic Algorithm as Feature selection technique, the best purity is obtained by hierarchical clustering, 97.50%, compared to K –means clustering, 96.75%, and Fuzzy C-means clustering at 94.00%.

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