scholarly journals A Novel Weighted Fuzzy C –Means Clustering Based on Immune Genetic Algorithm for Intrusion Detection

2012 ◽  
Vol 38 ◽  
pp. 1750-1757 ◽  
Author(s):  
S. Ganapathy ◽  
K. Kulothungan ◽  
P. Yogesh ◽  
A. Kannan
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Immune Genetic Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

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%.

Sign in / Sign up

Export Citation Format

Share Document