scholarly journals COMPONENT ANALYSIS FOR INTERESTING PATTERN DETECTION IN MULTI-VARIABLE DATA SETS

2021 ◽  
Vol 5 (1) ◽  
pp. 1-11
Author(s):  
Ahmet YÜCEL
Keyword(s):  
Component Analysis ◽  
Pattern Detection ◽  
Data Sets ◽  
Interesting Pattern ◽  
Variable Data

Human Performance Evaluation of a Metaphor Graphic Display for Respiratory Data

1994 ◽  
Vol 33 (04) ◽  
pp. 390-396 ◽  
Author(s):  
J. G. Stewart ◽  
W. G. Cole
Keyword(s):  
Performance Evaluation ◽  
Human Performance ◽  
Pattern Detection ◽  
Data Sets ◽  
Complex Task ◽  
Graphic Display ◽  
Literal Sense ◽  
Single Number ◽  
The Impact ◽  
Human Problem

Abstract:Metaphor graphics are data displays designed to look like corresponding variables in the real world, but in a non-literal sense of “look like”. Evaluation of the impact of these graphics on human problem solving has twice been carried out, but with conflicting results. The present experiment attempted to clarify the discrepancies between these findings by using a complex task in which expert subjects interpreted respiratory data. The metaphor graphic display led to interpretations twice as fast as a tabular (flowsheet) format, suggesting that conflict between earlier studies is due either to differences in training or to differences in goodness of metaphor, Findings to date indicate that metaphor graphics work with complex as well as simple data sets, pattern detection as well as single number reporting tasks, and with expert as well as novice subjects.

Strategies for reducing large fMRI data sets for independent component analysis

2006 ◽  
Vol 24 (5) ◽  
pp. 591-596 ◽  
Author(s):  
Ze Wang ◽  
Jiongjiong Wang ◽  
Vince Calhoun ◽  
Hengyi Rao ◽  
John A. Detre ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Data Sets

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.

scholarly journals Parallel group independent component analysis for massive fMRI data sets

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173496 ◽  
Author(s):  
Shaojie Chen ◽  
Lei Huang ◽  
Huitong Qiu ◽  
Mary Beth Nebel ◽  
Stewart H. Mostofsky ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Data Sets ◽  
Parallel Group ◽  
Group Independent Component Analysis

3000 Hits Lesson Overview

2017 ◽  
Vol 110 (9) ◽  
pp. 720
Author(s):  
Matthew Whitney
Keyword(s):  
Data Sets ◽  
Batting Average ◽  
Major League ◽  
Variable Data ◽  
Using Data

My favorite lesson is a statistics exploration using data from Major League Baseball's (MLB) 3000-Hits Club. Students in my introductorystatistics course analyze two 1-variable data sets—career batting average and total career hits—that have significant differences in variability and distribution (Wikimedia Foundation 2017a).

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

Identification of Excitation Source Number Using Principal Component Analysis

2011 ◽  
Vol 199-200 ◽  
pp. 850-857
Author(s):  
Jian Chao Dong ◽  
Tie Jun Yang ◽  
Xin Hui Li ◽  
Zhi Jun Shuai ◽  
You Hong Xiao
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Principal Component ◽  
Relevant Information ◽  
Component Analysis ◽  
Data Sets ◽  
Blind Signal ◽  
Input Multiple Output ◽  
Source Number

Principal component analysis (PCA), serving as one of the basic blind signal processing techniques, is extensively employed in all forms of analysis for extracting relevant information from confusing data sets. The principle of PCA is explained in this paper firstly, then the simulation and experiment are carried out to a simply supported beam rig, and PCA is used in frequency domain to identify sources number of several cases. Meanwhile principal components (PCs) contribution coefficient and signal to noise ratio between neighboring PCs (neighboring SNR) are introduced to cutoff minor components quantificationally. The results show that when observation number is equal to or larger than source number and additive noise is feebleness, accurate prediction of the number of uncorrelated excitation sources in a multiple input multiple output system could be obtained by principal component analysis.

Sign in / Sign up

Export Citation Format

Share Document