Improving Hierarchical Clustering of Genotypic Data via Principal Component Analysis

Crop Science ◽  
2013 ◽  
Vol 53 (4) ◽  
pp. 1546-1554 ◽  
Author(s):  
T. L. Odong ◽  
J. van Heerwaarden ◽  
T. J. L. van Hintum ◽  
F. A. van Eeuwijk ◽  
J. Jansen
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Principal Component ◽  
Component Analysis

Polluted aquifers: identification and characterisation by statistical analysis

2010 ◽  
Vol 61 (2) ◽  
pp. 331-337 ◽  
Author(s):  
Analía Boemo ◽  
Haydée Musso ◽  
Irene Lomniczi
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Underground Water ◽  
Chemical Components ◽  
Eastern Area ◽  
Western Area ◽  
Physical And Chemical

Hierarchical clustering and principal component analysis applied to chemical components and physicochemical properties of well water proved to be a useful tool for identification and characterisation of aquifers. Underground water of Lerma Valley (Salta, Argentina) was examined for its physical and chemical properties by sampling 46 wells located in two adjacent areas separated by hills, one of them polluted with boron since 1991. Hierarchical clustering splits sampled sites into two main clusters, corresponding to the two areas, establishing the fact that the aquifers should be considered as two different entities in spite of their common recharge area. Values of boron concentration in the eastern area decreased in most of the wells since the pollution sources were eradicated, while four of them experienced a substantial increase, proof of the slow self-recovery of the aquifer. The use of principal component analysis provided evidence of the incipient boron pollution of the aquifer of the western area.

Sex Assignment to Whole Human Responses in Rorschach

2017 ◽  
Vol 38 (2) ◽  
pp. 108-128
Author(s):  
Christian Mormont ◽  
Patrick Fontan
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Empirical Investigation ◽  
Principal Component ◽  
Component Analysis ◽  
Second Step ◽  
Chi Square ◽  
Sex Assignment ◽  
Formal Quality

Abstract. According to the theory of identification, men are more likely to qualify their Rorschach human content responses as males, and women as females. These assumptions were tested in an empirical investigation using a Belgian nonpatient sample of 800. All human responses and their location were listed. Analyses were carried out on the 10 Cards and on the formal quality (FQo vs. FQu/−) of all human responses according to the subject’s and the examiner’s sex. Variables were first submitted to principal component analysis, and resulting components were compared in a 2 × 2 design in order to assess examiners’ and participants’ sex potential effects on human responses sex assignments. Univariate and multivariate ANOVA revealed no or only negligible differences. In a second step, distributions of masculine, feminine, and neutral human responses across 16 card locations that commonly elicit human responses were submitted to hierarchical clustering in order to identify masculine, feminine, and neutral locations in Rorschach cards. Chi-square tests revealed no significant association between participants’ sex and human responses locations. Results do not corroborate predictions according to the theory of identification but they do, however, highlight the role of the distal features of blots.

Note on the Use of Principal Component Analysis (PCA) and Clustering for the Analysis of Wafer Level ATPG data

2006 ◽  
Author(s):  
Kartik Ramanujachar ◽  
Satish Draksharam
Keyword(s):  
Principal Component Analysis ◽  
Hierarchical Clustering ◽  
Principal Component ◽  
Component Analysis ◽  
Pattern Generation ◽  
Automatic Test Pattern Generation ◽  
Pca Analysis ◽  
Wafer Level ◽  
Failure Data ◽  
Commonality Analysis

Abstract This article explores the use of principal component analysis (PCA) and hierarchical clustering in the analysis of wafer level automatic test pattern generation (ATPG) failure data. The principle of commonality is extended by utilizing hierarchical clustering to collect die that are more similar to one another in their manner of failure than to others. Similarity is established by PCA of the patterns that the die in a wafer fail. Results demonstrated that PCA analysis and clustering are useful tools for dimensionality reduction and commonality analysis of wafer level ATPG data. The utility of PCA analysis and clustering in the extraction of die for physical failure analysis is also illustrated.

Landmark-Based Shape Analysis on Middle Cerebral Intracranial Aneurysms: A Geometric Morphometrics Approach to Infer Natural History

2020 ◽  
Vol 17 ◽  
Author(s):  
Vincent M. Tutino ◽  
Anthony J. Yan ◽  
Sricharan S. Veeturi ◽  
Tatsat R. Patel ◽  
Hamidreza Rajabzadeh-Oghaz ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Natural History ◽  
Geometric Morphometrics ◽  
Hierarchical Clustering ◽  
Intracranial Aneurysms ◽  
Shape Change ◽  
Principal Component ◽  
Component Analysis ◽  
Maximum Height ◽  
Cross Sectional

Background:: Due to scarcity of longitudinal data, the morphologic development of intracranial aneurysms (IAs) during their natural history remains poorly understood. However, longitudinal information can often be inferred from cross-sectional datasets as demonstrated by anatomists’ use of geometric morphometrics to build evolutionary trees, reconstructing species inter-relationships based on morphologic landmarks. Objective:: We adopted these tools to analyze cross-sectional image data and infer relationships between IA morphologies. Methods:: On 3D reconstructions of 52 middle cerebral artery (MCA) IAs (9 ruptured) and 10 IA-free MCAs (baseline geometries), 7 semi-automated landmarks were placed at the proximal parent artery and maximum height. From these, 64 additional landmarks were computationally generated to create a 71-landmark point cloud of 213 xyz coordinates. This data was normalized by Procrustes transformation and used in principal component analysis, hierarchical clustering, and phylogenetic analyses. Results:: Principal component analysis showed separation of IA-free MCA geometries and grouping of ruptured IAs from unruptured IAs. Hierarchical clustering delineated a cluster of only unruptured IAs that were significantly smaller and more spherical than clusters that had ruptured IAs. Phylogenetic classification placed ruptured IAs more distally in the tree than unruptured IAs, indicating greater shape derivation. Groups of unruptured IAs were observed, but ruptured IAs were invariably found in mixed lineages with unruptured IAs, suggesting that some pathways of shape change may be benign while other are more associated with rupture. Conclusion:: Geometric morphometric analyses of larger datasets may indicate particular pathways of shape change leading toward aneurysm rupture versus stabilization.

Sign in / Sign up

Export Citation Format

Share Document