Clustering methods
A Comparative Study on CT Image Segmentation Using FCM-based Clustering Methods
... and clustering methods for CT-image clustering, the following experiments are ...Four clustering algorithms are implemented; they are FCM, KM, FCMm, and ...These clustering algorithms ...
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A comparison of clustering methods for biogeography with fossil datasets
... these methods are quite different. For example, methods may be hierarchical or non-hierarchical in their approaches, and may use Euclidean distance or non-Euclidean indices to cluster the ...different ...
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De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units
... DGC methods appear to perform as well or better than the hierarchical clustering ...DGC methods utilized the USEARCH program and the DGC method is used for clustering in UPARSE (Edgar, 2010; ...
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The use of different clustering methods in the evaluation of genetic diversity in upland cotton
... Silva Filho et al. (2005) were able to verify genetic variability among evaluated cultivars of upland cotton, it being possible to identify divergent genotypes having good technological characteristics for their fibre, ...
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Validity studies among hierarchical methods of cluster analysis using cophenetic correlation coefficient
... However, clustering techniques are tools where the application and interpretation are subjective, depending on the experience and user perspicacity ...Different clustering methods produce different ...
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Mapping topographic structure in white matter pathways with level set trees.
... density clustering [39] for complex fiber tractography from a high- angular resolution form of ...agglomerative clustering methods, the output of our procedure is a hierarchy of clusters that can be ...
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Neutrosophic Hierarchical Clustering Algoritms
... hierarchical clustering pro- cedure, the single valued neutrosophic aggregation operator, and the basic distance measures between ...for clustering interval neutrosophic ...another clustering ...
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Inferring modules from human protein interactome classes
... Results: We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome ...
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Evaluation of Genetic Divergence among Lines of Laying Hens using Cluster Analysis
... by clustering methods are adequately ...the clustering obtained by the optimization method may be evaluated by the discriminant analysis of the obtained data, generally based on the methodology of ...
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Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
... unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various ...
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Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)
... a clustering of data the data items into disjoint groups is obtained ...known methods for clustering is K-means developed by Mac Queen in ...partition clustering method that separates data ...
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An Efficient Way for Clustering Using Alternative Decision Tree
... results clustering algorithms, two of which most important is, clustering performance and meaningful cluster ...of clustering method is available, among those hierarchical clustering and ...
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Historical Invasion Records Can Be Misleading: Genetic Evidence for Multiple Introductions of Invasive Raccoons (Procyon lotor) in Germany.
... Biological invasions provide excellent study systems to understand evolutionary, genetic and ecological processes during range expansions. There is strong evidence for positive ef- fects of high propagule pressure and ...
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Bilateral Symmetry Information for Brain Tumor Detection
... of methods and algorithms are available to deal with the problem of segmentation of ...(1) Clustering Methods, (2) Thresholding Methods, (3) Edge- Detection Methods, and (4) ...
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Clustering Assisted Fundamental Matrix Estimation
... The paper proposes the utilization of clustering to optimize the corresponding pairs of points for accurate estimation of the fundamental matrix. This approach chooses the point pairs which are likely to represent ...
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Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach
... K-means, which is a fast and popular method, is used to perform clustering, because it easily manages huge amounts of data. The basic intuition behind K-means is the continuous reassignment of objects into ...
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Clustering of resting state networks.
... Surface maps of the seven clusters that were found using the clustering algorithm and random initialization are shown in Figure 3. Axial slices at the levels of subcortical gray matter are shown in Figure 4. They ...
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Algorithms for Clustering on the Sphere: Advances & Applications
... for clustering data on the sphere that is based on the Poisson ...allow clustering of data that reside in these ...based clustering is used, an extensive study of the role of initializa- tion on the ...
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Energy Efficient Adaptive Protocol for Clustered Wireless Sensor Networks
... Fig.1. extracted from [8] describes the architecture of wireless sensor networks structure. In this paper, we focus on the energy efficient clustering algorithm for event-driven wireless sensor network. We propose ...
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Remodularization analysis using semantic clustering
... Durante a evolução de software, sua estrutura inevitavelmente se torna mais difícil de manter, a menos que um esforço explícito de manutenção seja feito para melhorá-la. No intuito de resolver esse problema, a ...
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