parallel coordinates
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2021 ◽  
Vol 12 (1) ◽  
pp. 331
Author(s):  
Szymon Bobek ◽  
Sławomir K. Tadeja ◽  
Łukasz Struski ◽  
Przemysław Stachura ◽  
Timoleon Kipouros ◽  
...  

We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others.


Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3203
Author(s):  
Ádám Ipkovich ◽  
Károly Héberger ◽  
János Abonyi

A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates.


2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Camila R. Lopes ◽  
Lúcio F. D. Santos ◽  
Daniel L. Jasbick ◽  
Daniel De Oliveira ◽  
Marcos Bedo

A diversified similarity search retrieves elements that are simultaneously similar to a query object and akin to the different collections within the explored data. While several methods in information retrieval, data clustering, and similarity searching have tackled the problem of adding diversity into result sets, the experimental comparison of their performances is still an open issue mainly because the quality metrics are “borrowed” from those different research areas, bringing their biases alongside. In this manuscript, we investigate a series of such metrics and experimentally discuss their trends and limitations. We conclude diversity is better addressed by a set of measures rather than a single quality index and introduce the concept of Diversity Features Model (DFM), which combines the viewpoints of biased metrics into a multidimensional representation. Experimental evaluations indicate (i) DFM enables comparing different result diversification algorithms by considering multiple criteria, and (ii) the most suitable searching methods for a particular dataset are spotted by combining DFM with ranking aggregation and parallel coordinates maps.


Author(s):  
Timoleon Kipouros ◽  
Ibrahim Chamseddine ◽  
Michael Kokkolaras

Abstract Nanoparticle drug delivery better targets neoplastic lesions than free drugs and thus has emerged as safer form of cancer therapy. Nanoparticle design variables are important determinants of efficacy as they influence the drug biodistribution and pharmacokinetics. Previously, we determined optimal designs through mechanistic modeling and optimization. However, the numerical nature of the tumor model and numerous candidate nanoparticle designs hinder hypothesis generation and treatment personalization. In this paper, we utilize the parallel coordinates technique to visualize high-dimensional optimal solutions and extract correlations between nanoparticle design and treatment outcomes. We found that at optimality, two major design variables are dependent, and thus the optimization problem can be reduced. In addition, we obtained an analytical relationship between optimal nanoparticle sizes and optimal distribution, which could facilitate the utilization of tumors models in preclincal studies. Our approach has simplified the results of the previously integrated modeling and optimization framework developed for nanotherapy and enhanced the interpretation and utilization of findings. Integrated mathematical frameworks are increasing in the medical field, and our method can be applied outside nanotherapy to facilitate clinical translation of computational methods.


2021 ◽  
Author(s):  
Felipe Marx Benghi ◽  
Luiz Gomes-Jr

Outlying Aspect Mining (OAM) is a new way of handling outliers that, instead of focusing solely on the detection, also provides an explanation. This is done by presenting a subspace of attributes that had the most abnormal behavior. Acknowledging this group of attributes is important but only listing them is not sufficient for a human specialist to comprehend the situation and take the necessary actions. A higher-level, visual approach can improve the process, providing better cognitive clues to experts. Here we describe a Visual Analytics platform developed to present data and OAM outputs in a human-friendly interface. A novelty available on this platform is a parallel coordinates plot that also display temporal multidimensional data. Such representation overcome human visual system limitations and helps in the outlier investigation. To explore the applicability of the developed tool, a locomotive operation user case is employed with focus on fault analysis in an OAM point of view.


AIAA Journal ◽  
2021 ◽  
pp. 1-15
Author(s):  
Sławomir K. Tadeja ◽  
Timoleon Kipouros ◽  
Yupu Lu ◽  
Per Ola Kristensson

2021 ◽  
pp. 101361
Author(s):  
Omar Alminagorta Cabezas ◽  
Charlie Loewen ◽  
Derrick T. de Kerckhove ◽  
Donald A. Jackson ◽  
Cindy Chu

2021 ◽  
Author(s):  
Zhiguang Zhou ◽  
Yuming Ma ◽  
Yong Zhang ◽  
Yuhua Liu ◽  
Xiaorong Yang ◽  
...  

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