scholarly journals ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

2015 ◽  
Vol 43 (W1) ◽  
pp. W566-W570 ◽  
Author(s):  
Tauno Metsalu ◽  
Jaak Vilo
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Web Tool

Entwicklung, Beobachtung und Steuerung integrierter, quasi-kontinuierlicher pharmazeutischer Produktionsprozesse mit Methoden der Multivariaten Datenverarbeitung

2016 ◽  
Author(s):  
Sven-Oliver Borchert
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Process Analytical Technology ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Analytical Technology

Die vorliegende Arbeit befasst sich mit Aspekten einer modernen Bioverfahrenstechnik am ­Beispiel von Prozessen zur Herstellung rekombinanter potentieller Malariavakzine. Dabei ­wurden zwei quasi-kontinuierliche Prozesse aus herkömmlichen Batch-Unit Operationen auf­gebaut, in denen die Anwendung von Process Analytical Technology im Vordergrund steht. Das Hauptaugenmerk dieser Arbeit lag dabei auf einer Implementierung der Multivariate Data ­Analysis zum Monitoring und zur Evaluierung des zyklischen Prozessablaufes und seiner Reproduzierbarkeit. Im Bereich der Principal Component Analysis wurde die Methode der Prozessüberwachung mit dem Golden Batch-Tunnel angewendet. Mit dem Golden Batch-Ansatz ­wurden Methoden zur Prozessprädiktion implementiert und mit einer Model Predictive Multi­variate Control auch zur Steuerung von realen Prozesses erprobt. Darüber hinaus wurde die MVDA zur Prädiktion von Medienkomponenten sowie deren zellspezifische Reaktionsraten aus klassischen Onli...

scholarly journals Penggunaan Analisis Komponen Utama Dalam Penggabungan Data Peubah Ganda pada Kasus Produksi Pertanian dan Perkebunan Di Wilayah Bolaang Mongondow Tahun 2008

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (2) ◽  
pp. 1 ◽  
Author(s):  
Sunarsi Habib Abdurrachman ◽  
Hanny Komalig ◽  
Nelson Nainggolan
Keyword(s):  
Principal Component Analysis ◽  
Initial Data ◽  
Principal Components ◽  
Multivariate Data ◽  
Secondary Data ◽  
Principal Component ◽  
Component Analysis ◽  
The North ◽  
North Sulawesi ◽  
Production Agriculture

Abstract The objective of this research is to study the combine the two groups of data with multivariate variables using Principal Component Analysis. The data used in this study is a secondary data drawn from the North Sulawesi BPS data in Production Agriculture and Plantation Bolaang Mongondow region in 2008. The results show that PCA can be used to combining two separate groups multivariate data and the correlation between the Principal Components of the data are combined with the Principal Component of the overall initial data (intact) is relatively high wich correlation between PC1 and PC1AB as big 0,987 and correlation between PC2 and PC2AB as big 0,916. Keywords : Principal Component Analysis, Agriculture Production and Plantation Abstrak Tujuan penelitian ini adalah menggabungkan dua gugus data peubah ganda dengan menggunakan Analisis Komponen Utama. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diambil dari BPS Sulawesi Utara yakni Data Produksi Pertanian Dan Perkebunan Di Wilayah Bolaang Mongondow Tahun 2008. Hasilnya menunjukkan bahwa AKU dapat digunakan untuk menggabungkan dua gugus data peubah ganda yang terpisah dan korelasi antara komponen utama dari data yang digabungkan dengan komponen utama dari keseluruhan data awal (utuh)  relatif tinggi yakni dengan nilai korelasi PC1 dan PC1AB sebesar 0,987 dan PC2 dan PC2AB  sebesar 0,916.   Kata kunci : Analisis Komponen Utama, Produksi Pertanian dan Perkebunan

scholarly journals The Principal Component Analysis Biplot Predictions versus the Ordinary Least Squares Regression Predictions: The Anthropometric Case Study

2019 ◽  
pp. 1-10
Author(s):  
Chisimkwuo John ◽  
Chukwuemeka O. Omekara ◽  
Godwin Okwara
Keyword(s):  
Principal Component Analysis ◽  
Medical Center ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Least Squares Regression ◽  
Data Set ◽  
Ols Regression

An indicative feature of a principal component analysis (PCA) variant to the multivariate data set is the ability to transform correlated linearly dependent variables to linearly independent principal components. Back-transforming these components with the samples and variables approximated on a single calibrated plot gives rise to the PCA Biplots. In this work, the predictive property of the PCA biplot was augmented in the visualization of anthropometric measurements namely; weight (kg), height (cm), skinfold (cm), arm muscle circumference AMC (cm), mid upper arm circumference MUAC (cm) collected from the students of School of Nursing and Midwifery, Federal Medical Center (FMC), Umuahia, Nigeria. The adequacy and quality of the PCA Biplot was calculated and the predicted samples are then compared with the ordinary least square (OLS) regression predictions since both predictions makes use of an indicative minimization of the error sum of squares. The result suggests that the PCA biplot prediction merits further consideration when handling correlated multivariate data sets as its predictions with mean square error (MSE) of 0.00149 seems to be better when compared to the OLS regression predictions with MSE of 29.452.

scholarly journals Multivariate statistical analysis of the essential oils of five Beilschmiediaspecies from Peninsular Malaysia

2021 ◽  
Vol 20 (1) ◽  
pp. 61-70
Author(s):  
Wan Mohd Nuzul Hakimi Wan Salleh ◽  
◽  
Shazlyn Milleana Shaharudin ◽  
Keyword(s):  
Principal Component Analysis ◽  
Gas Chromatography ◽  
Data Analysis ◽  
Essential Oils ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Components ◽  
Herbal Products

Identification of the chemical compositionof essential oils is very important for ensuring the quality of finished herbal products. The objective of the study was to analyze the chemical components present in the essential oils of five Beilschmiediaspecies (i.e. B. kunstleri, B. maingayi, B. penangiana, B. madang, and B. glabra) by multivariate data analysis using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The essential oils were obtained by hydrodistillation and fully characterized by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). A total of 108 chemical components were successfully identified from the essential oils of five Beilschmiediaspecies. The essential oils were characterized by high proportions of β-caryophyllene (B.kunstleri), δ-cadinene (B. penangianaand B. madang), and β-eudesmol (B. maingayiand B. glabra). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that chemical similarity was highest for all samples, except for B. madang. The multivariate data analysis may be used for the identification and characterization of essential oils from different Beilschmiediaspecies that are to be used as raw materials of traditional herbal products.

scholarly journals Efficiency assessment of e-learning compared to traditional education using Principal Component Analysis and verification of Genetic Algorithms: تقييم كفاءة التعليم الإلكتروني مقارنة بالتعليم التقليدي باستخدام تحليل المكونات الأساسية والتحقق بالخوارزميات الجينية

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Firas Shawkat Hamid
Keyword(s):  
Principal Component Analysis ◽  
Genetic Algorithms ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Technological Development ◽  
Multivariate Data ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
E Learning

Multivariate data analysis is one of the common techniques that are used in the analysis of the main compounds that perform the process of converting a large number of related variables into a smaller number of unrelated compounds, In the case of the emergence of anomalous values, which can be detected in many ways, the adoption of the matrix of contrast and common contrast will lead to misleading results in the analysis of the principal compounds. Therefore, many of the phenomena that consist of a large group of variables that are difficult to deal with initially, and the process of interpreting these variables becomes a complex process, so reducing these variables to a lower setting is easier to deal with, and it is the aspiration of every researcher working in the field of main compounds analysis or factor analysis. Because of technological development and the ability to communicate by audio and video interaction at the same time, on this research, a multivariate data collection process was conducted, where an evaluation of the efficiency of e-learning was studied and analyzed by highlighting the process of analyzing real data using factor analysis by the Principal Component Analysis method. This is one of the techniques used to summarize and shorten the data and through the use of the SPSS: Statistical Packages for Social Sciences Program, Thus, it will be noted that the subject of the paper will flow into the concept of Data mining also, And then achieve it using genetic algorithms using the simulation program with its final version, which is MATLAB, also using the method of Multiple Linear Regression Procedure to find the arrangement of independent variables by calculating the weight of the independent variable. Total results were obtained for the eigenvalues of the stored correlation matrix or the rotating factor matrix, The study required conducting statistical analysis in the mentioned way and by reducing the number of variables without losing much information about the original variables and its aim is to simplify its understanding and reveal its structure and interpretation, The study required conducting statistical analysis in the mentioned way and by reducing the number of variables without losing much information about the original variables and its aim is to simplify its understanding and reveal its structure and interpretation. In addition to reaching a set of conclusions that were discussed in detail also the addition to the important recommendations.

scholarly journals A Method of L1-Norm Principal Component Analysis for Functional Data

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 182
Author(s):  
Fengmin Yu ◽  
Liming Liu ◽  
Nanxiang Yu ◽  
Lianghao Ji ◽  
Dong Qiu
Keyword(s):  
Principal Component Analysis ◽  
Big Data ◽  
Functional Data ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
L1 Norm ◽  
Principal Component Analysis Method ◽  
L2 Norm

Recently, with the popularization of intelligent terminals, research on intelligent big data has been paid more attention. Among these data, a kind of intelligent big data with functional characteristics, which is called functional data, has attracted attention. Functional data principal component analysis (FPCA), as an unsupervised machine learning method, plays a vital role in the analysis of functional data. FPCA is the primary step for functional data exploration, and the reliability of FPCA plays an important role in subsequent analysis. However, classical L2-norm functional data principal component analysis (L2-norm FPCA) is sensitive to outliers. Inspired by the multivariate data L1-norm principal component analysis methods, we propose an L1-norm functional data principal component analysis method (L1-norm FPCA). Because the proposed method utilizes L1-norm, the L1-norm FPCs are less sensitive to the outliers than L2-norm FPCs which are the characteristic functions of symmetric covariance operator. A corresponding algorithm for solving the L1-norm maximized optimization model is extended to functional data based on the idea of the multivariate data L1-norm principal component analysis method. Numerical experiments show that L1-norm FPCA proposed in this paper has a better robustness than L2-norm FPCA, and the reconstruction ability of the L1-norm principal component analysis to the original uncontaminated functional data is as good as that of the L2-norm principal component analysis.

scholarly journals Visualizing the Complexity of the Athlete-Monitoring Cycle Through Principal-Component Analysis

2019 ◽  
Vol 14 (9) ◽  
pp. 1304-1310 ◽  
Author(s):  
Dan Weaving ◽  
Clive Beggs ◽  
Nicholas Dalton-Barron ◽  
Ben Jones ◽  
Grant Abt
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Data Sets ◽  
Complex Data ◽  
Great Loss ◽  
Athlete Monitoring ◽  
Multiple Variables

Purpose: To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing complex multivariate data sets associated with the training of athletes. Conclusions: Using PCA, it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PCs), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (ie, variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA, it is possible to visualize complex data sets containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete-monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process.

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