Second visualizations for the datasets’ density maps
TreeVersity: Interactive Visualizations for Comparing Hierarchical Datasets
... The overview of the publications subjects and authors on Figure10↓, show that most of the subjects presented increases in the second part of the decade, especially with respect to common subjects like "Highways". ...
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Exploratory visualizations and statistical analysis of large, heterogeneous epigenetic datasets
... Abstract Epigenetic marks, such as DNA methylation and histone modifications, are important regulatory mechanisms that allow a single genomic sequence to give rise to a complex multicellular organism. When studying ...
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Deceptive visualizations for time series datasets: an experiment on y-axis manipulation
... Two treatments were designed as two separate questionnaires that could be reached by two different links. Both links were circulated at the same time period through same channels: Facebook, Twitter, LinkedIn, and e-mail. ...
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Fast Emulation of Self-organizing Maps for Large Datasets
... The second level of k-means for prototype clustering Phase 2 involves second application of k-means ...The second level k-means of phase 2 performs the same initialization process for centroids but ...
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Scalable Varied Density Clustering Algorithm for Large Datasets
... Figure 6. Natural number of clusters in dataset We have used many synthetic datasets to test the proposed algorithm. The experiments have been done on seven different datasets containing 2D points. The ...
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A Declarative Rendering Model for Multiclass Density Maps
... per second, which would take hours or days to run through millions of ...If density significantly varies (e.g., population maps), some regions of the map become empty, while others remain ...
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Dynamic Query Visualizations on World Wide Web Clients: A DHTML Solution for Maps and Scattergrams
... The second challenge is the more lingering and interesting one. Due to the nature of how we construct the result image using multiple layers, there is a time overhead for updates that would grow with the number of ...
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Efficient incremental density-based algorithm for clustering large datasets
... 5). Second, it provides what is called ‘‘stability ” ; the partitioning algorithm reaches stability by utilizing a learning rate that decays with time so that objects are consistently assigned to centroids and the ...
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An Optimized Density-based Algorithm for Anomaly Detection in High Dimensional Datasets
... high-volume datasets efficiently. To handle such datasets, new algorithms need to be proposed or the existing algorithms should be ...high-dimensional datasets based on all these fore-mentioned ...
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Characterizing Uncertainty in High-Density Maps from Multiparental Populations
... physical maps in progress toward full sequence ...population. Second, the compu- tational burden of mapping thousands of markers per chro- mosome limits analysis to the fast two-point methods (Wu et ...
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Interactive tag maps and tag clouds for the multiscale exploration of large spatio-temporal datasets
... therefore precomputed a hierarchical set of clusters to achieve the levels of interaction required. In order to compute real-time spatial prominence, we implemented a computationally-efficient algorithm that counts tags ...
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USING SELF-ORGANIZING MAPS FOR INFORMATION VISUALIZATION AND KNOWLEDGE DISCOVERY IN COMPLEX GEOSPATIAL DATASETS
... and explanation. The SOM is used as a combination of clustering and projection techniques for features extraction, visualization and interpretation of large high-dimensional datasets. The first level of the ...
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Selective Mapping: A Strategy for Optimizing the Construction of High-Density Linkage Maps
... sizes were required for a given performance ratio and much smaller sample can be selected for subsequent the sizes needed to approach a performance ratio of mapping that very nearly minimizes the necessary sacri- 1.0 ...
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Health assessment of trees using GPR-derived root density maps
... the density and the distribution of roots using ground penetrating radar is ...The second step consists of a tracking algorithm that aims at identifying patterns that resemble tree ...the density of ...
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Quantification of masking risk in screening mammography with volumetric breast density maps
... the density map where the dense tissue thickness exceeded 1 cm; and (3) a dense tissue masking model (DTMM) in which the size distri- bution and cancer location probability are taken into ...the second ...
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User studies in cartography: opportunities for empirical research on interactive maps and visualizations
... Fabrikant, 2009; Haklay & Zafiri, 2008; Ooms et al., 2015; Roth et al., 2011) and ‘reanalysis’ articles applying a range of techniques to a compilation of previously published studies as a new, methodological ...
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Improved density peak clustering for large datasets
... local density peaks that are sufficiently distant one from the ...small datasets only and is highly sensitive to the value of tunable ...Improved Density Peak Clustering (IDPC), a new algorithm ...
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GIPSY: Joining Spatial Datasets with Contrasting Density
... GIS datasets, to add the branches of one Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed ...
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Density Based Feature Selection Method for Medical Datasets
... respectively. Thus there is a reduction in the dimensionality of the original datasets that in turn help in increasing the accuracy of prediction and classification. The performance metrics of the three ...
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Prioritizing global marine mammal habitats using density maps in place of range maps
... range maps as input implicitly assume uniform habitat use, treating areas of high and low density as equiva- lent, and disregarding important differences between core and marginal ...range maps are ...
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