scholarly journals On Rohlf′s Method for the Detection of Outliers in Multivariate Data

1995 ◽  
Vol 52 (2) ◽  
pp. 295-307 ◽  
Author(s):  
C. Caroni ◽  
P. Prescott
Keyword(s):  
Multivariate Data ◽  
Detection Of Outliers

scholarly journals A Review on Outliers-Detection Methods for Multivariate Data

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Sharifah Sakinah Syed Abd Mutalib ◽  
Siti Zanariah Satari ◽  
Wan Nur Syahidah Wan Yusoff
Keyword(s):  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Outlier Detection ◽  
High Dimension ◽  
Detection Method ◽  
Multivariate Data ◽  
Projection Pursuit ◽  
Detection Methods ◽  
And Cluster Analysis ◽  
Detection Of Outliers

Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed.

scholarly journals A Novel Approach for Outlier Detection in Multivariate Data

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Saima Afzal ◽  
Ayesha Afzal ◽  
Muhammad Amin ◽  
Sehar Saleem ◽  
Nouman Ali ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Multivariate Data ◽  
Evaluation Criteria ◽  
Real Life ◽  
Simulation Experiments ◽  
Novel Approach ◽  
Multiple Variables ◽  
Components Analysis ◽  
Detection Of Outliers ◽  
F Measure

Outlier detection is a challenging task especially when outliers are defined by rare combinations of multiple variables. In this paper, we develop and evaluate a new method for the detection of outliers in multivariate data that relies on Principal Components Analysis (PCA) and three-sigma limits. The proposed approach employs PCA to effectively perform dimension reduction by regenerating variables, i.e., fitted points from the original observations. The observations lying outside the three-sigma limits are identified as the outliers. This proposed method has been successfully employed to two real life and several artificially generated datasets. The performance of the proposed method is compared with some of the existing methods using different performance evaluation criteria including the percentage of correct classification, precision, recall, and F-measure. The supremacy of the proposed method is confirmed by abovementioned criteria and datasets. The F-measure for the first real life dataset is the highest, i.e., 0.6667 for the proposed method and 0.3333 and 0.4000 for the two existing approaches. Similarly, for the second real dataset, this measure is 0.8000 for the proposed approach and 0.5263 and 0.6315 for the two existing approaches. It is also observed by the simulation experiments that the performance of the proposed approach got better with increasing sample size.

Man-Computer Derivations of Tree Structure from Multivariate Data

1968 ◽  
Author(s):  
Gerald H. Shure ◽  
Laurence I. Press ◽  
Miles S. Rogers
Keyword(s):  
Multivariate Data ◽  
Tree Structure

Metabolic Profiling of Maca (Lepidium meyenii) Cultivars using NMR and Multivariate Data Analysis

Planta Medica ◽  
2010 ◽  
Vol 76 (05) ◽  
Author(s):  
J Zhao ◽  
B Avula ◽  
C Clément ◽  
M Kreuzera ◽  
IA Khan
Keyword(s):  
Data Analysis ◽  
Metabolic Profiling ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Lepidium Meyenii

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

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