scholarly journals Comprehensive Service Level Analysis of Online Taxi Drivers Based on Fuzzy Clustering Combined with Principal Component Analysis

2019 ◽  
Author(s):  
Hong Chen ◽  
Chan Li
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Service Level ◽  
Taxi Drivers ◽  
Comprehensive Service ◽  
Level Analysis

scholarly journals COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS

Kursor ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annisa Eka Haryati ◽  
Sugiyarto Sugiyarto ◽  
Rizki Desi Arindra Putri
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Subtractive Clustering ◽  
Validity Index ◽  
Fuzzy Clustering Method ◽  
Dependent Variables ◽  
Better Than

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.

Gender classification based on fuzzy clustering and principal component analysis

2016 ◽  
Vol 10 (3) ◽  
pp. 228-233 ◽  
Author(s):  
Hamid Hassanpour ◽  
Amin Zehtabian ◽  
Avishan Nazari ◽  
Hossein Dehghan
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Gender Classification

Fault Diagnosis of Grounding Grid Based on Principal Component Analysis and Fuzzy Clustering

2013 ◽  
Vol 787 ◽  
pp. 881-885
Author(s):  
Ke Xin Zhao ◽  
Min Fang Peng ◽  
Hu Tan ◽  
Shu Di He ◽  
Mei E Shen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Fault Location ◽  
Principal Component ◽  
Component Analysis ◽  
Test Point ◽  
Cluster Center ◽  
Economic Losses ◽  
Grounding Grid

The problem of grounding grid fault causes great economic losses, so accurate and efficient fault location is becoming more important. This paper puts forward a new method of fault diagnosis for grounding network.Taking voltage values of test point as fault characteristics and making use of principal component analysis extract fault features from training and test samples, which can eliminate the correlation between the fault symptoms.Taking fuzzy clustering for the samples after feature extraction can get clustering center. By testing sample membership of each sample and each cluster center can diagnose the fault. The outcomes verify that utilizing principal component analysis and fuzzy clustering to solving the fault location of grounding network has good diagnostic effectiveness and efficiency.

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