Selection of Cluster Hierarchy Depth in Hierarchical Clustering Using K-Means Algorithm

2007 ◽  
Author(s):  
Shinwon Lee ◽  
Wonhee Lee ◽  
Sungjong Chung ◽  
Dongun An ◽  
Ingeun Bok ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Selection Of

scholarly journals Network Analysis Based on Unique Spectral Features Enables an Efficient Selection of Genomically Diverse Operational Isolation Units

2021 ◽  
Vol 9 (2) ◽  
pp. 416
Author(s):  
Charles Dumolin ◽  
Charlotte Peeters ◽  
Evelien De Canck ◽  
Nico Boon ◽  
Peter Vandamme
Keyword(s):  
Network Analysis ◽  
Hierarchical Clustering ◽  
Mass Spectra ◽  
Spectral Features ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
Diversity Studies ◽  
Technical Sample ◽  
Efficient Selection ◽  
Selection Of

Culturomics-based bacterial diversity studies benefit from the implementation of MALDI-TOF MS to remove genomically redundant isolates from isolate collections. We previously introduced SPeDE, a novel tool designed to dereplicate spectral datasets at an infraspecific level into operational isolation units (OIUs) based on unique spectral features. However, biological and technical variation may result in methodology-induced differences in MALDI-TOF mass spectra and hence provoke the detection of genomically redundant OIUs. In the present study, we used three datasets to analyze to which extent hierarchical clustering and network analysis allowed to eliminate redundant OIUs obtained through biological and technical sample variation and to describe the diversity within a set of spectra obtained from 134 unknown soil isolates. Overall, network analysis based on unique spectral features in MALDI-TOF mass spectra enabled a superior selection of genomically diverse OIUs compared to hierarchical clustering analysis and provided a better understanding of the inter-OIU relationships.

scholarly journals A GIS Anchored System for Selection of Utility Service Stations through Hierarchical Clustering

2013 ◽  
Vol 10 ◽  
pp. 762-772 ◽  
Author(s):  
Anirban Chakraborty ◽  
J.K. Mandal ◽  
S.B. Chandrabanshi ◽  
S. Sarkar
Keyword(s):  
Hierarchical Clustering ◽  
Service Stations ◽  
Selection Of

Selection of Representative Constituents for Unknown, Variable, Complex, or Biological Origin Substance Assessment Based on Hierarchical Clustering

2021 ◽  
Author(s):  
Darina G. Yordanova ◽  
Timothy J. Patterson ◽  
Colin M. North ◽  
Louise Camenzuli ◽  
Atanas S. Chapkanov ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Biological Origin ◽  
Unknown Variable ◽  
Selection Of

Research on Fault Diagnosis Method of Steam Turbine Unit Based on the Theory of Super Ball

2014 ◽  
Vol 496-500 ◽  
pp. 953-957
Author(s):  
Xiao Wen Deng ◽  
Yan Peng Han ◽  
Cheng Cheng Wang ◽  
Yu Jiong Gu
Keyword(s):  
Fault Diagnosis ◽  
Steam Turbine ◽  
Hierarchical Clustering ◽  
Turbine Unit ◽  
Thermal Parameters ◽  
Steam Turbine Unit ◽  
Diagnosis Method ◽  
Density Clustering ◽  
Selection Of

A fault diagnosis method of steam turbine based on the theory of super ball is proposed, which combines density clustering with hierarchical clustering. The correlation of vibration and thermal parameters is introduced as the clustering factors. The efficiency of diagnosis,the sensitivity of noise and the accuracy of diagnosis are improved. Experiments show that the method and the selection of clustering factor are feasible.

Principal Component and Genetic Diversity Analysis for Seed Yield and Its Related Components in the Genotypes of Chickpea (Cicer arietinum L.)

2021 ◽  
Author(s):  
S.K. Jain ◽  
L.D. Sharma ◽  
K.C. Gupta ◽  
Vipin Kumar ◽  
R.S. Sharma
Keyword(s):  
Genetic Diversity ◽  
Plant Height ◽  
Total Variation ◽  
Hierarchical Clustering ◽  
Seed Yield ◽  
Block Design ◽  
Principal Component ◽  
Experimental Unit ◽  
Component Traits ◽  
Selection Of

Background: The seed yield of chickpea can be improved by selection of superior genotypes on the basis of different yield and yield component traits. These genotypes exclusively utilize in breeding programs. Yield is a complex trait which is affected by several factors, hence, a well-known technique known as principal component analysis was used to identify and minimize the number of traits for effective selection. To obtain efficient recombinants, the identified component traits need to be combined from diverse parents through recombination breeding followed by selection of transgressive segregants. Hence, the present study is envisaged to measure the genetic diversity among genotypes of chickpea.Methods: The experimental material comprised of 40 chickpea genotypes evaluated in randomized block design with three replications. The experimental unit was four rows per plot with 4 m length and spacing between row to row and plant to plant maintained as 30 x 10 cm. NPK (20:40:00) fertilizers was applied as basal doses. The data were recorded for each genotype on nine quantitative traits as per standard methods. Descriptive statistics and PCA analysis was performed by using the statistical package SPSS 16.0 version and cluster analysis was done using the Wards method of hierarchical clustering technique.Result: Out of nine PCs only three PCs exhibited more than 1.0 Eigen value and showed about 73.4% variability. PC1 contributed 28.6% of the total variation and correlated with days to flowering, days to maturity, plant height, first pod height, seeds per pod and number of pods per plant while PC2 explained 21.00% of the total variation and dominated by plant height, first pod height and seed yield. PC3 explained an additional 13.00% of the total variation and dominated by primary branches per plant. Genotype commonly found in more PC, were BG 4016, IPCB 2015-165, IPC 2011-247, GNG2459 and RKG 19-4. Hierarchical clustering technique grouped 40 genotypes into two main clusters (A and B) and nine sub clusters. The present investigation depicted that the chickpea germplasm displayed considerable genetic diversity for most of the traits under consideration. 

Cognitive gadgets and cognitive priors

2019 ◽  
Vol 42 ◽  
Author(s):  
Gian Domenico Iannetti ◽  
Giorgio Vallortigara
Keyword(s):  
Cognitive Neuroscience ◽  
Selection Of

Abstract Some of the foundations of Heyes’ radical reasoning seem to be based on a fractional selection of available evidence. Using an ethological perspective, we argue against Heyes’ rapid dismissal of innate cognitive instincts. Heyes’ use of fMRI studies of literacy to claim that culture assembles pieces of mental technology seems an example of incorrect reverse inferences and overlap theories pervasive in cognitive neuroscience.

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