Hierarchical Cluster Analysis: Comparison of Three Linkage Measures and Application to Psychological Data

Yim, Odilia; Ramdeen, Kylee T.

doi:10.20982/tqmp.11.1.p008

Cited by 420 publications

(300 citation statements)

References 15 publications

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“…Squared Euclidean distance was used in the proximities matrix [15,16]. Weighted average linkage was used as the clustering method since the size of the clusters was not expected to be similar [15,16].…”

Section: Cluster Analysismentioning

confidence: 99%

Categories of Wistar Rats Based on Anxiety Traits: A Study Using Factor and Cluster Method

Radhakrishnan

Gulia

2018

Ann Neurosci

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Background: Outcomes of neurobehavioral studies are invariably affected by the variations in the anxiety traits of animals in the same species. Identifying those traits and categorizing them accordingly would improve the reproducibility of results, and reduce the variation in the results from different laboratories. Purpose: The present study was done to identify the possible groups among the normal population of outbred adult male Wistar rats. Methods: Anxiety traits were measured in elevated plus maze (EPM) test and open field test (OFT). The various anxiety responses from these tests were subjected to exploratory factor and hierarchical cluster analyses. Different clusters thus derived were compared with each other. Results and Conclusion: In exploratory factorial analysis, 2 components, that is, anxiety and activity were derived from the EPM and OFT parameters. Cluster analysis of EPM parameters classified the rats into 3 groups “high anxiety and low activity”, “medium anxiety and high activity”, and “low anxiety and medium activity”. Whereas, cluster analysis on OFT parameters identified one more group namely “low anxiety and high activity”. The rats which came under the clusters formed from the EPM and OFT parameters were not identical. Moreover, EPM and OFT may be measuring different aspects of anxiety.

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Section: Cluster Analysismentioning

confidence: 99%

Categories of Wistar Rats Based on Anxiety Traits: A Study Using Factor and Cluster Method

Radhakrishnan

Gulia

2018

Ann Neurosci

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“…Character states from the characters obtained in the study were assigned values and were subjected to hierarchical cluster analysis in SPSS Version 23 to generate a dendrogram showing linkages between the species based on their character values. The statistical method used was average linkage between groups which is also referred to as UPGMA [33]. This is the same statistical method used in the analysis of MatK and Rbcl sequences.…”

Section: Cluster Analysis Based On Leafmentioning

confidence: 99%

Phylogeny of Ten Kenyan Plectranthus Species in the Coleus Clade Inferred from Leaf Micromorphology, Rbcl and MatK Genes

et al. 2017

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Plectranthus species are difficult to taxonomically delimit due to lack of clear-cut morphological synapomorphies. This study is aimed at bringing insights into classification of ten Plectranthus species in the Coleus clade by using leaf micromorphology and molecular data. Stomatal counts and observation of microtome leaf sections generated leaf micromorphology data, while molecular data was obtained from sequencing MatK and Rbcl genes from each species. Phylogeny based on the MatK and Rbcl gene sequences clustered four species P. caninus, P. otostegioides, P. barbatus, and P. lanuginosus together (Clusters A and D, respectively), while P. pseudomarrubioides, P. ornatus, and P. aegyptiacus were grouped together into Clusters B and E, respectively, and P. montanus and P. amboinicus were grouped together (Cluster C). A dendrogram was generated through a cluster analysis of the leaf micromorphological characters grouped together, P. caninus, P. ornatus, P. otostegioides, P. montanus, and P. pseudomarrubioides (Cluster F). The dendrogram also grouped together P. aegyptiacus, P. amboinicus, P. edulis, P. barbatus, and P. lanuginosus (Cluster G). The present study has grouped the ten studied Plectranthus species using molecular and leaf micromorphology characters into phylogenies, which are supported by previous studies, and proved that these characters can aid in plant identification and phylogenetic studies.

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“…At each stage, the corresponding HCA coefficient indicates the distance between the two clusters. A large difference between the coefficients of two consecutive stages indicates that the clusters being merged possess increasing heterogeneity so to terminate the merging process before the clusters become too different [66]. As the merging process being terminated, the final number of clusters can be obtained by deducting the value of the terminated stage from the total number of stations.…”

Section: Hierarchical Cluster Analysis (Hca)mentioning

confidence: 99%

Exploring Long Term Spatial Vegetation Trends in Taiwan from AVHRR NDVI3g Dataset Using RDA and HCA Analyses

2016

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Due to 4000 m elevation variation with temperature differences equivalent to 50 degrees of latitudinal gradient, exploring Taiwan's spatial vegetation trends is valuable in terms of diverse ecosystems and climatic types covering a relatively small island with an area of 36,000 km 2 . This study analyzed Taiwan's spatial vegetation trends with controlling environmental variables through redundancy (RDA) and hierarchical cluster (HCA) analyses over three decades of monthly normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) NDVI3g data for 19 selected weather stations over the island. Results showed two spatially distinct vegetation response groups. Group 1 comprises weather stations which remained relatively natural showing a slight increasing NDVI tendency accompanied with rising temperature, whereas Group 2 comprises stations with high level of human development showing a slight decreasing NDVI tendency associated with increasing temperature-induced moisture stress. Statistically significant controlling variables include climatic factors (temperature and precipitation), orographic factors (mean slope and aspects), and anthropogenic factor (population density). Given the potential trajectories for future warming, variable precipitation, and population pressure, challenges, such as land-cover and water-induced vegetation stress, need to be considered simultaneously for establishing adequate adaptation strategies to combat climate change challenges in Taiwan.

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Hierarchical Cluster Analysis: Comparison of Three Linkage Measures and Application to Psychological Data

Cited by 420 publications

References 15 publications

Categories of Wistar Rats Based on Anxiety Traits: A Study Using Factor and Cluster Method

Categories of Wistar Rats Based on Anxiety Traits: A Study Using Factor and Cluster Method

Phylogeny of Ten Kenyan Plectranthus Species in the Coleus Clade Inferred from Leaf Micromorphology, Rbcl and MatK Genes

Exploring Long Term Spatial Vegetation Trends in Taiwan from AVHRR NDVI3g Dataset Using RDA and HCA Analyses

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