A New Soft Sensor Based on Dynamic Principal Component Analysis and on-line Potential Fuzzy Clustering

2008 ◽  
Author(s):  
Karim Amanian ◽  
Karim Salahshoor ◽  
Mohammad Reza Jafari ◽  
Mohsen Mosallaie
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Soft Sensor ◽  
Dynamic Principal Component Analysis ◽  
On Line

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.

Adaptive Principal Component Analysis-Based Outliers Detection Through Neighborhood Voting in Wireless Sensor Networks

2019 ◽  
pp. 255-285
Author(s):  
Ekaterina Aleksandrova ◽  
Christos Anagnostopoulos
Keyword(s):  
Principal Component Analysis ◽  
Statistical Learning ◽  
Variance Estimation ◽  
Principal Component ◽  
Component Analysis ◽  
Power Method ◽  
Environmental Sensors ◽  
On Line ◽  
Computing Environments ◽  
Adaptive Principal Component Analysis

This chapter introduces statistical learning methods and findings of a group decision-making algorithm in internet of things (IoT) and edge computing environments. The discussed methodology locally detects outliers in an on-line and adaptive mode. It is driven by three perspectives—opinion, confidence, and independence—and exploits the incremental principal component analysis using the power method for eigenvector and eigenvalue estimation and Knuth and Welford's online algorithms for variance estimation. The methodology is implemented and evaluated over real contextual data in a wireless network of environmental sensors from where appropriate conclusions are drawn about the capabilities and limitations of the proposed solution in IoT environments.

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