scholarly journals K-means method with linear search algorithm to reduce Means Square Error (MSE) within data clustering

2018 ◽  
Vol 434 ◽  
pp. 012032
Author(s):  
S Sriadhi ◽  
S Gultom ◽  
M Martiano ◽  
R Rahim ◽  
D Abdullah
Keyword(s):  
Data Clustering ◽  
Search Algorithm ◽  
Linear Search

Automatic data clustering using nature-inspired symbiotic organism search algorithm

2019 ◽  
Vol 163 ◽  
pp. 546-557 ◽  
Author(s):  
Yongquan Zhou ◽  
Haizhou Wu ◽  
Qifang Luo ◽  
Mohamed Abdel-Baset
Keyword(s):  
Data Clustering ◽  
Search Algorithm ◽  
Automatic Data ◽  
Symbiotic Organism Search

scholarly journals Feature Selection in Decision Systems: A Mean-Variance Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chengdong Yang ◽  
Wenyin Zhang ◽  
Jilin Zou ◽  
Shunbo Hu ◽  
Jianlong Qiu
Keyword(s):  
Feature Selection ◽  
Data Clustering ◽  
Search Algorithm ◽  
Evaluation Function ◽  
Uncertainty Measure ◽  
Greedy Search ◽  
Decision Systems ◽  
Mean Variance ◽  
Variance Approach ◽  
Experiment Analysis

Uncertainty measure is an important implement for characterizing the degree of uncertainty. It has been extensively applied in pattern recognition and data clustering. Because of instability of traditional uncertainty measures, mean-variance measure (MVM) is utilized to perform feature selection, which could depress disturbances and noises effectively. Thereby, a novel evaluation function based on MVM is designed. The forward greedy search algorithm (FGSA) with the proposed evaluation function is exploited to perform feature selection. Experiment analysis shows the validity and effectiveness of MVM.

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