scholarly journals Applying Visual Analysis Procedures to Multidimensional Medical Data

2019 ◽  
Author(s):  
Александр Бондарев ◽  
Aleksandr Bondarev ◽  
Владимир Галактионов ◽  
Vladimir Galaktionov
Keyword(s):  
Preliminary Data ◽  
Visual Analysis ◽  
Original Data ◽  
Medical Data ◽  
Multidimensional Data ◽  
Data Sets ◽  
Data Filtering ◽  
Data Points ◽  
Multidimensional Data Sets

The paper considers the tasks of visual analysis of multidimensional data sets of medical origin. For visual analysis, the approach of building elastic maps is used. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. To improve the results, a number of previously developed procedures are used - preliminary data filtering, removal of separated clusters (flotation). To solve the scalability problem, when the elastic map is adjusted both to the region of condensation of data points and to separately located points of the data cloud, the quasi-Zoom approach is applied. The illustrations of applying elastic maps to various sets of medical data are presented.

scholarly journals THE PROCEDURES OF VISUAL ANALYSIS FOR MULTIDIMENSIONAL DATA VOLUMES

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 17-21 ◽  
Author(s):  
A. E. Bondarev
Keyword(s):  
Data Processing ◽  
Visual Analysis ◽  
Original Data ◽  
Multidimensional Data ◽  
Data Sets ◽  
Multidimensional Datasets ◽  
Data Points ◽  
Multidimensional Data Sets

<p><strong>Abstract.</strong> The paper is devoted to problems of visual analysis of multidimensional data sets using an approach based on the construction of elastic maps. This approach is quite suitable for processing and visualizing of multidimensional datasets. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the structure of multidimensional dataset. The paper presents the results of applying elastic maps for visual analysis of multidimensional data sets of medical origin. Previously developed data processing procedures are applied to improve the results obtained - pre-filtering of data, removal of separated clusters (flotation), quasi-Zoom.</p>

scholarly journals A comparative user study of visualization techniques for cluster analysis of multidimensional data sets

2020 ◽  
Vol 19 (4) ◽  
pp. 318-338 ◽  
Author(s):  
Elio Ventocilla ◽  
Maria Riveiro
Keyword(s):  
User Study ◽  
Quality Measures ◽  
Multidimensional Data ◽  
Data Sets ◽  
Data Set ◽  
Novice Users ◽  
Data Points ◽  
Perceived Usability ◽  
Multidimensional Data Sets ◽  
Cluster Quality

This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, [Formula: see text], embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of [Formula: see text] as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with [Formula: see text] values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward’s hierarchical clustering) are likely to lead to estimates of [Formula: see text] that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.

Empirical Orthogonal Functions

2011 ◽  
Author(s):  
Gidon Eshel
Keyword(s):  
Missing Values ◽  
Original Data ◽  
Empirical Orthogonal Functions ◽  
Basis Set ◽  
Data Matrix ◽  
Multidimensional Data ◽  
Data Sets ◽  
Eof Analysis ◽  
Orthogonal Functions ◽  
Multidimensional Data Sets

This chapter focuses on empirical orthogonal functions (EOFs). One of the most useful and common eigen-techniques in data analysis is the construction of EOFs. EOFs are a transform of the data; the original set of numbers is transformed into a different set with some desirable properties. In this sense the EOF transform is similar to other transforms, such as the Fourier or Laplace transforms. In all these cases, we project the original data onto a set of functions, thus replacing the original data with the set of projection coefficients on the chosen new set of basis vectors. However, the choice of the specific basis set varies from case to case. The discussions cover data matrix structure convention, reshaping multidimensional data sets for EOF analysis, forming anomalies and removing time mean, missing values, choosing and interpreting the covariability matrix, calculating the EOFs, projection time series, and extended EOF analysis.

scholarly journals Visualization of Multidimensional Data in Nursing Science

2016 ◽  
Vol 39 (1) ◽  
pp. 112-126 ◽  
Author(s):  
Sharron L. Docherty ◽  
Allison Vorderstrasse ◽  
Debra Brandon ◽  
Constance Johnson
Keyword(s):  
Visual Analysis ◽  
Scientific Visualization ◽  
Data Gathering ◽  
Population Level ◽  
Multidimensional Data ◽  
Data Sets ◽  
Large Sets ◽  
Health And Illness ◽  
History Of ◽  
Multidimensional Data Sets

Nursing scientists have long been interested in complex, context-dependent questions addressing individual- and population-level challenges in health and illness. These critical questions require multilevel data (e.g., genetic, physiologic, biologic, behavioral, affective, and social). Advances in data-gathering methods have resulted in the collection of large sets of complex, multifaceted, and often non-comparable data. Scientific visualization is a powerful methodological tool for facilitating understanding of these multidimensional data sets. Our purpose is to demonstrate the utility of scientific visualization as a method for identifying associations, patterns, and trends in multidimensional data as exemplified in two studies. We describe a brief history of visual analysis, processes involved in scientific visualization, and opportunities and challenges in the use of visualization methods. Scientific visualization can play a crucial role in helping nurse scientists make sense of the structure and underlying patterns in their data to answer vital questions in the field.

scholarly journals VISUAL ANALYSIS AND PROCESSING OF CLUSTERS STRUCTURES IN MULTIDIMENSIONAL DATASETS

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 151-154 ◽  
Author(s):  
A. E. Bondarev
Keyword(s):  
Visual Analysis ◽  
A Priori ◽  
Cluster Structure ◽  
Original Data ◽  
Medical Data ◽  
A Priori Information ◽  
Multidimensional Datasets ◽  
Data Volume ◽  
Data Points ◽  
Priori Information

The article is devoted to problems of visual analysis of clusters structures for a multidimensional datasets. For visual analyzing an approach of elastic maps design [1,2] is applied. This approach is quite suitable for processing and visualizing of multidimensional datasets. To analyze clusters in original data volume the elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the cluster structure of multidimensional dataset. The approach of elastic maps does not require any a priori information about data in question and does not depend on data nature, data origin, etc. Elastic maps are usually combined with PCA approach. Being presented in the space based on three first principal components the elastic maps provide quite good results. The article describes the results of elastic maps approach application to visual analysis of clusters for different multidimensional datasets including medical data.

EPOS-Norway Portal

2021 ◽  
Author(s):  
Jan Michalek ◽  
Kuvvet Atakan ◽  
Christian Rønnevik ◽  
Helga Indrøy ◽  
Lars Ottemøller ◽  
...  
Keyword(s):  
Web Services ◽  
Visual Analysis ◽  
Earth Science ◽  
National Research Council ◽  
Geophysical Data ◽  
Multidimensional Data ◽  
Data Sets ◽  
Complex Data ◽  
Submarine Landslides ◽  
Data Repositories

&lt;p&gt;The European Plate Observing System (EPOS) is a European project about building a pan-European infrastructure for accessing solid Earth science data, governed now by EPOS ERIC (European Research Infrastructure Consortium). The EPOS-Norway project (EPOS-N; RCN-Infrastructure Programme - Project no. 245763) is a Norwegian project funded by National Research Council. The aim of the Norwegian EPOS e&amp;#8209;infrastructure is to integrate data from the seismological and geodetic networks, as well as the data from the geological and geophysical data repositories. Among the six EPOS-N project partners, four institutions are providing data &amp;#8211; University of Bergen (UIB), - Norwegian Mapping Authority (NMA), Geological Survey of Norway (NGU) and NORSAR.&lt;/p&gt;&lt;p&gt;In this contribution, we present the EPOS-Norway Portal as an online, open access, interactive tool, allowing visual analysis of multidimensional data. It supports maps and 2D plots with linked visualizations. Currently access is provided to more than 300 datasets (18 web services, 288 map layers and 14 static datasets) from four subdomains of Earth science in Norway. New datasets are planned to be integrated in the future. EPOS-N Portal can access remote datasets via web services like FDSNWS for seismological data and OGC services for geological and geophysical data (e.g. WMS). Standalone datasets are available through preloaded data files. Users can also simply add another WMS server or upload their own dataset for visualization and comparison with other datasets. This portal provides unique way (first of its kind in Norway) for exploration of various geoscientific datasets in one common interface. One of the key aspects is quick simultaneous visual inspection of data from various disciplines and test of scientific or geohazard related hypothesis. One of such examples can be spatio-temporal correlation of earthquakes (1980 until now) with existing critical infrastructures (e.g. pipelines), geological structures, submarine landslides or unstable slopes. &amp;#160;&lt;/p&gt;&lt;p&gt;The EPOS-N Portal is implemented by adapting Enlighten-web, a server-client program developed by NORCE. Enlighten-web facilitates interactive visual analysis of large multidimensional data sets, and supports interactive mapping of millions of points. The Enlighten-web client runs inside a web browser. An important element in the Enlighten-web functionality is brushing and linking, which is useful for exploring complex data sets to discover correlations and interesting properties hidden in the data. The views are linked to each other, so that highlighting a subset in one view automatically leads to the corresponding subsets being highlighted in all other linked views.&lt;/p&gt;

scholarly journals Variable selection study using Procrustes analysis

2013 ◽  
Vol 1 (1) ◽  
pp. 7 ◽  
Author(s):  
Casimiro S. Munita ◽  
Lúcia P. Barroso ◽  
Paulo M.S. Oliveira
Keyword(s):  
Analytical Techniques ◽  
Procrustes Analysis ◽  
Multidimensional Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Essential Information ◽  
Selection Of Variables ◽  
Archaeological Ceramic ◽  
Multidimensional Data Sets

Several analytical techniques are often used in archaeometric studies, and when used in combination, these techniques can be used to assess 30 or more elements. Multivariate statistical methods are frequently used to interpret archaeometric data, but their applications can be problematic or difficult to interpret due to the large number of variables. In general, the analyst first measures several variables, many of which may be found to be uninformative, this is naturally very time consuming and expensive. In subsequent studies the analyst may wish to measure fewer variables while attempting to minimize the loss of essential information. Such multidimensional data sets must be closely examined to draw useful information. This paper aims to describe and illustrate a stopping rule for the identification of redundant variables, and the selection of variables subsets, preserving multivariate data structure using Procrustes analysis, selecting those variables that are in some senses adequate for discrimination purposes. We provide an illustrative example of the procedure using a data set of 40 samples in which were determined the concentration of As, Ce, Cr, Eu, Fe, Hf, La, Na, Nd, Sc, Sm, Th, and U obtained via instrumental neutron activation analysis (INAA) on archaeological ceramic samples. The results showed that for this data set, only eight variables (As, Cr, Fe, Hf, La, Nd, Sm, and Th) are required to interpret the data without substantial loss information.

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