Examples of using Hierarchical clustering in English and their translations into Portuguese
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Multivariate analysis was performed using hierarchical clustering based on average euclidean distance.
Hierarchical clustering algorithms(hc) construct a cluster hierarchy also known as dendrogram.
These hierarchies are usually obtained through unsupervised hierarchical clustering algorithms.
The hierarchical clustering method was the last innovation in noise separation in ssa approach, implemented on package r-ssa.
Figure 1 Sanitary district clusters according to sociodemographic characteristics, obtained by the hierarchical clustering technique.
Specific protocols are also hierarchical clustering of items, but their goal is to contemplate the specific data of the researched subject.
By using these variables in the cluster analysis of SDs,the result of the hierarchical clustering showed that the SD can be grouped Figure 1.
Hierarchical clustering algorithms(hc) produce a hierarchy of nested clustering, organized as a hierarchical tree.
In this paper we use an approach to image segmentation using a hierarchical clustering method based on the minimum spanning tree.
By means of hierarchical clustering, patients who have similar patterns across symptoms can be grouped into clusters and subgroups can be formed on the basis of symptom experience.
His work continued on visual analysis tools for time series data, TimeSearcher,high dimensional data, Hierarchical Clustering Explorer, and social network data, SocialAction.
One of them, known as HHC(Heuristic-based Hierarchical Clustering), was introduced in 2007 and began to be used by the DBLP, the same database that Daniel Figueiredo used in his study, as it was one of the simpler tools available to deal with the problem.
Thus the data obtained were used in the rating analysis of principal component analysis and hierarchical clustering, where it was possible to differentiate the samples collected at each location.
As can be seen in Table 3, one-way ANOVA with post hoc Bonferroni correction showed significant associations between questionnaire domains andsymptom severity as defined by hierarchical clustering.
Various procedures and statistical techniques used were:ward's method to identify hierarchical clustering; the analysis of variance(anova and manova) to check similarities and dissimilarities between the clusters; nonparametric wilcoxon signed-ranks.
Examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps(SOMs), hierarchical clustering, and consensus clustering methods.
Analysis of scores hierarchical clustering and residual analysis of the contingency table showed more severe involvement of CU in areas relating to symptoms and feelings questions 1 and 2, daily activities question 3 and leisure question 5.
This dissertation is mainly focused on the investigation of unsupervised machine learning algorithms identified as hierarchical clustering algorithms, particularly those that fall under the subcategory of agglomerative clustering. .
In sequence,(ii) studies the relationship of four indicators(population, income, generation and gravimetric composition) in 31 municipal solid waste collection sectors, using for this,multivariate techniques such as principal component analysis and hierarchical clustering.
Since the traditional approach of dea game only provides efficiency rates this study will also make a cluster analysis using hierarchical clustering technique, known ward¿s method, in order to obtain realistic benchmarks.
However, in the context of semi-supervised hierarchical clustering, the works in the literature do not efficient explore the selection of cases(instances or cluster) to add constraints, neither the interaction of the user with the clustering process.
The clusters represented by the dendrogram are similar to the findings of the tolerance distance methodology. Note, however,that the clusters formed by the hierarchical clustering methodology include categories that are apparently more distant.
Hierarchical clustering, a multivariate exploratory data analysis method, is frequently applied to the analysis of such datasets and may contribute to the understanding of the mechanism of action of unknown drugs by comparison with the effects obtained for known drugs.
Seven strategic variables were obtained after the factors analysis and used to determinate five strategic groups of stores.they were discovered by the sequential use of hierarchical clustering and k-means including stores with different profiles and performances measured in volume and quality.
For this purpose, stiff diagrams andstatistical analysis of hierarchical clustering were applied in hydrochemical monitoring data of 26 wells, totaling 158 samples, collected between the years 2010 and 2014, and data from pumping tests were used in groundwater flow simulations for modeling the transport of contaminants in the vadose and saturated zones.
In the present work, 1h-nmr spectra and transversal relaxation time(t2) distributions of a brazilian crude oil sample collection, ranging from light to extra-heavy,were measured and analyzed by hierarchical clustering analysis(hca) and principal component analysis pca.
Iron oxides, indicators of pedogenic factors, including the dominant lithology in source material, reveal diagnostic pedo-environmental characteristics,even as a basis for hierarchical clustering, and also, in many circumstances thereof, the agricultural potential of soils, particularly in tropical areas of the globe.
The purposes of the present investigation were to assess the chemical composition of apis mellifera l. propolis samples collected in different regions of mato grosso do sul state, comprising the cerrado, pantanal and atlantic forest biomes,to analyze their similarities by principal component analysis( pca) and hierarchical clustering analysis( hca), to evaluate their antioxidant and antitumor potential and to investigate the contribution of baccharis dracunculifolia dc as a botanical source for these samples.
Hierarchical cluster analysis was performed in the present study.
The ascending or hierarchical cluster method starts with individual objects, i.e. the subjects.