Driver Stress Recognition for Smart Transportation: Applying Multiobjective Genetic Algorithm for Improving Fuzzy C-Means Clustering with Reduced Time and Model Complexity

2022 ◽  
pp. 100668
Author(s):  
Kwok Tai Chui
Keyword(s):  
Genetic Algorithm ◽  
Model Complexity ◽  
Smart Transportation ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Driver Stress ◽  
Reduced Time ◽  
Stress Recognition

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%.

Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

2021 ◽  
pp. 1-9
Author(s):  
Saumya Singh ◽  
Smriti Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Data Analysis ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Data Sets ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

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