Parallel Coordinates Research Articles

Assessing clutter reduction in parallel coordinates using image processing techniques

Assessing clutter reduction in parallel coordinates using image processing techniques

SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining.

Biclustering extracts coexpressed genes under certain experimental conditions, providing more precise insight into the genetic behaviors than one-dimensional clustering. For understanding the biological features of genes in a single bicluster, visualizations such as heatmaps or parallel coordinate plots and tools for enrichment analysis are widely used. However, simultaneously handling many biclusters still remains a challenge. Thus, we developed a web service named SiBIC, which, using maximal frequent itemset mining, exhaustively discovers significant biclusters, which turn into networks of overlapping biclusters, where nodes are gene sets and edges show their overlaps in the detected biclusters. SiBIC provides a graphical user interface for manipulating a gene set network, where users can find target gene sets based on the enriched network. This chapter provides a user guide/instruction of SiBIC with background of having developed this software. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/sibic/faces/index.jsp .

Batch process control and monitoring: a Dual STATIS and Parallel Coordinates (DS-PC) approach

ABSTRACTMultivariate data collected from batches is usually monitored via control charts (CCs) based on MPCA and MPLS for batch to batch comparison. In addition, distribution free approaches include other dimensionality reduction methods for batch and time-wise analysis. However, techniques for multivariate data focused on variable-wise analysis haven’t been widely developed. Here, we propose a nonparametric quality control strategy for off-line monitoring of batches and variables, besides visual clustering of observations within batches. In our approach, CCs based on Dual STATIS are created using robust bagplots to enhance signal detection in batch and variable-wise analysis, while parallel coordinate plots are used in identification of unusual observations’ behavior per variable, regardless distributional assumptions. This proposed strategy poses the main advantage of detecting different type of changes through meaningful visualization tools, allowing easier interpretation of results in industrial settings.

WIZUALIZACJA DANYCH JAKOŚCIOWYCH Z WYKORZYSTANIEM PAKIETU EXTRACAT PROGRAMU R

Visualization in research process plays a crucial role. There are several advanced plots for visualizing categorical data, such as mosaic, association, double-decker, sieve or fourfold plot that are based on the graphical presentation of residuals in a contingency table. In this paper we present new methods for visualizing categorical data such as rmb, fluctile and scpcp plot available in extracat package in R. This package provides a well-structured representation of categorical data and allows for a detailed presentation of the relationship between categories in terms of proportions. We describe rmb, fluctile and cpcp. Those plots are based on the concept of multiple bar charts, a fluctuation diagram from a multidimensional table and parallel coordinates respectively. Such plots are mostly used for a visualization of a contingency table or a data frame; they can also be used for exploratory analysis and allows for a graphical presentation even for a high number of variables [Pilhofer, Unwin 2013]. All the calculations and plots are obtained using R software.

Role of Melt Curve Analysis in Interpretation of Nutrigenomics' MicroRNA Expression Data.

This article illustrates the importance of melt curve analysis (MCA) in interpretation of mild nutrogenomic micro(mi)RNA expression data, by measuring the magnitude of the expression of key miRNA molecules in stool of healthy human adults as molecular markers, following the intake of Pomegranate juice (PGJ), functional fermented sobya (FS), rich in potential probiotic lactobacilli, or their combination. Total small RNA was isolated from stool of 25 volunteers before and following a three-week dietary intervention trial. Expression of 88 miRNA genes was evaluated using Qiagen's 96 well plate RT2 miRNA qPCR arrays. Employing parallel coordinates plots, there was no observed significant separation for the gene expression (Cq) values, using Roche 480® PCR LightCycler instrument used in this study, and none of the miRNAs showed significant statistical expression after controlling for the false discovery rate. On the other hand, melting temperature profiles produced during PCR amplification run, found seven significant genes (miR-184, miR-203, miR-373, miR-124, miR-96, miR-373 and miR-301a), which separated candidate miRNAs that could function as novel molecular markers of relevance to oxidative stress and immunoglobulin function, for the intake of polyphenol (PP)-rich, functional fermented foods rich in lactobacilli (FS), or their combination. We elaborate on these data, and present a detailed review on use of melt curves for analyzing nutigenomic miRNA expression data, which initially appear to show no significant expressions, but are actually more subtle than this simplistic view, necessitating the understanding of the role of MCA for a comprehensive understanding of what the collective expression and MCA data collectively imply.

How to Read Many-Objective Solution Sets in Parallel Coordinates [Educational Forum

Rapid development of evolutionary algor ithms in handling many-objective optimization problems requires viable methods of visualizing a high-dimensional solution set. The parallel coordinates plot which scales well to high-dimensional data is such a method, and has been frequently used in evolutionary many-objective optimization. However, the parallel coordinates plot is not as straightforward as the classic scatter plot to present the information contained in a solution set. In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives. We hope that these observations could provide some guidelines as to the proper use of the parallel coordinates plot in evolutionary manyobjective optimization.

Cluster-aware arrangement of the parallel coordinate plots

The dimension ordering of parallel coordinate plots has been widely studied, aiming at the insightful exploration of multi-dimensional data. However, few works focus on the category distributions across dimensions and construct an effective dimension ordering to enable the visual exploration of clusters. Therefore, we propose a cluster-aware arrangement method of the parallel coordinate plots and design a visualization framework for the multi-dimensional data exploration. Firstly, a hierarchical clustering scheme is employed to identify the categories of interest across different dimensions. Then we design a group of icicle views to present the hierarchies of dimensions, the colors of which also indicate the relationships between different categories. A cluster-aware correlation is defined to measure the relationships between different attribute axes, based on the distributions of categories. Furthermore, a matrix map is designed to present the relationships between dimensions, and the MDS method is employed to transform the dimensions into 2D coordinates, in which the correlations among the dimensions are conserved. At last, we solve the Traveling Salesman Problem (TSP) and achieve an automated dimension ordering of the parallel coordinate plots, which largely highlights the relations of categories across dimensions. A set of convenient interactions are also integrated in the visualization system, allowing users to get insights into the multi-dimensional data from various perspectives. A large number of experimental results and the credible user studies further demonstrate the usefulness of the cluster-aware arrangement of the parallel coordinate plots.

High-dimensional data analysis with subspace comparison using matrix visualization

Due to the intricate relationship between different dimensions of high-dimensional data, subspace analysis is often conducted to decompose dimensions and give prominence to certain subsets of dimensions, i.e. subspaces. Exploring and comparing subspaces are important to reveal the underlying features of subspaces, as well as to portray the characteristics of individual dimensions. To date, most of the existing high-dimensional data exploration and analysis approaches rely on dimensionality reduction algorithms (e.g. principal component analysis and multi-dimensional scaling) to project high-dimensional data, or their subspaces, to two-dimensional space and employ scatterplots for visualization. However, the dimensionality reduction algorithms are sometimes difficult to fine-tune and scatterplots are not effective for comparative visualization, making subspace comparison hard to perform. In this article, we aggregate high-dimensional data or their subspaces by computing pair-wise distances between all data items and showing the distances with matrix visualizations to present the original high-dimensional data or subspaces. Our approach enables effective visual comparisons among subspaces, which allows users to further investigate the characteristics of individual dimensions by studying their behaviors in similar subspaces. Through subspace comparisons, we identify dominant, similar, and conforming dimensions in different subspace contexts of synthetic and real-world high-dimensional data sets. Additionally, we present a prototype that integrates parallel coordinates plot and matrix visualization for high-dimensional data exploration and incremental dimensionality analysis, which also allows users to further validate the dimension characterization results derived from the subspace comparisons.

Correlation analysis of respiratory signals by using parallel coordinate plots

Background and objectivesThe understanding of the bonds and the relationships between the respiratory signals, i.e. the airflow, the mouth pressure, the relative temperature and the relative humidity during breathing may provide the improvement on the measurement methods of respiratory mechanics and sensor designs or the exploration of the several possible applications in the analysis of respiratory disorders. Therefore, the main objective of this study was to propose a new combination of methods in order to determine the relationship between respiratory signals as a multidimensional data. MethodsIn order to reveal the coupling between the processes two very different methods were used: the well-known statistical correlation analysis (i.e. Pearson’s correlation and cross-correlation coefficient) and parallel coordinate plots (PCPs). Curve bundling with the number intersections for the correlation analysis, Least Mean Square Time Delay Estimator (LMS-TDE) for the point delay detection and visual metrics for the recognition of the visual structures were proposed and utilized in PCP. ResultsThe number of intersections was increased when the correlation coefficient changed from high positive to high negative correlation between the respiratory signals, especially if whole breath was processed. LMS-TDE coefficients plotted in PCP indicated well-matched point delay results to the findings in the correlation analysis. Visual inspection of PCB by visual metrics showed range, dispersions, entropy comparisons and linear and sinusoidal-like relationships between the respiratory signals. ConclusionIt is demonstrated that the basic correlation analysis together with the parallel coordinate plots perceptually motivates the visual metrics in the display and thus can be considered as an aid to the user analysis by providing meaningful views of the data.

3D-RadVis Antenna: Visualization and performance measure for many-objective optimization

Abstract So far the focus of almost all multi- or many-objective performance metrics has been the convergence and distribution of solutions in the objective space (Pareto-surface). Pareto-surface metrics such as IGD, HV, and Spread are simple and provide knowledge about the overall performance of the solution set. However, these measures do not provide any insight into the distribution or spread of a solution set with respect to each objective. Further, in many-objective optimization, visualization of true Pareto fronts or obtained non-dominated solutions is difficult. A proper visualization tool must be able to show the location, range, shape, and distribution of obtained non-dominated solutions (both Pareto-surface and objective-wise distribution). Existing commonly used visualization tools in many-objective optimization (e.g., parallel coordinates) fail to show the shape of the Pareto front or distribution of solutions along each objective. In this paper, we propose an extension of recently proposed visualization method called 3D-RadVis (we call it 3D-RadVis Antenna) to visualize the distribution of solutions along each objective. 3D-RadVis Antenna is capable of mapping M-dimensional objective space to a 3-dimensional radial coordinate plot while seeking to preserve the relative location of solutions, shape of the Pareto front, and distribution of solutions along each objective. Furthermore, 3D-RadVis Antenna can be used by decision-makers to visually navigate large many-objective solution sets, to observe the evolutionary process, to visualize the relative location of a solution, to evaluate trade-offs among objectives, and to select preferred solutions. Along with this visualization tool, we propose two novel performance measures, named objective-wise inverse generational distance (ObjIGD) and line distribution ( ∆ Line ) to measure the convergence and distribution of solutions along each objective as well as the overall performance of approximate solutions. The effectiveness of the proposed methods are demonstrated on widely used many-objective benchmark problems containing a variety of Pareto fronts (linear, concave, convex, mixed, and disconnected). In addition, for a case study, we have demonstrated the capability of 3D-RadVis Antenna combined with the proposed performance measures for visual progress tracking of the NSGA-III algorithm through generations. Experimental results show that the proposed visualization method can effectively be used to compare and track the performance of many-objective algorithms. Moreover, the proposed measures can be used as reliable complementary measures along with other widely used performance measures to compare many-objective solution sets.

A computational approach for phenotypic comparisons of cell populations in high-dimensional cytometry data.

Cytometry is an experimental technique used to measure molecules expressed by cells at a single cell resolution. Recently, several technological improvements have made possible to increase greatly the number of cell markers that can be simultaneously measured. Many computational methods have been proposed to identify clusters of cells having similar phenotypes. Nevertheless, only a limited number of computational methods permits to compare the phenotypes of the cell clusters identified by different clustering approaches. These phenotypic comparisons are necessary to choose the appropriate clustering methods and settings. Because of this lack of tools, comparisons of cell cluster phenotypes are often performed manually, a highly biased and time-consuming process. We designed CytoCompare, an R package that performs comparisons between the phenotypes of cell clusters with the purpose of identifying similar and different ones, based on the distribution of marker expressions. For each phenotype comparison of two cell clusters, CytoCompare provides a distance measure as well as a p-value asserting the statistical significance of the difference. CytoCompare can import clustering results from various algorithms including SPADE, viSNE/ACCENSE, and Citrus, the most current widely used algorithms. Additionally, CytoCompare can generate parallel coordinates, parallel heatmaps, multidimensional scaling or circular graph representations to visualize easily cell cluster phenotypes and the comparison results. CytoCompare is a flexible analysis pipeline for comparing the phenotypes of cell clusters identified by automatic gating algorithms in high-dimensional cytometry data. This R package is ideal for benchmarking different clustering algorithms and associated parameters. CytoCompare is freely distributed under the GPL-3 license and is available on https://github.com/tchitchek-lab/CytoCompare.

Mathematical Abstraction: Constructing Concept of Parallel Coordinates

Mathematical abstraction is an important process in teaching and learning mathematics so pre-service mathematics teachers need to understand and experience this process. One of the theoretical-methodological frameworks for studying this process is ion in Context (AiC). Based on this framework, abstraction process comprises of observable epistemic actions, Recognition, Building-With, Construction, and Consolidation called as RBC + C model. This study investigates and analyzes how pre-service mathematics teachers constructed and consolidated concept of Parallel Coordinates in a group discussion. It uses AiC framework for analyzing mathematical abstraction of a group of pre-service teachers consisted of four students in learning Parallel Coordinates concepts. The data were collected through video recording, students’ worksheet, test, and field notes. The result shows that the students’ prior knowledge related to concept of the Cartesian coordinate has significant role in the process of constructing Parallel Coordinates concept as a new knowledge. The consolidation process is influenced by the social interaction between group members. The abstraction process taken place in this group were dominated by empirical abstraction that emphasizes on the aspect of identifying characteristic of manipulated or imagined object during the process of recognizing and building-with.

Interactive optimization for the planning of urban systems

This paper introduces URBio, an interactive optimization framework for the planning of urban systems. Addressing the elusive nature of urban planning and its need to cope with more technical sectors, the framework allows urban planners to generate and evaluate many alternative urban configurations, while focusing their attention on the most promising ones. First, addressing the need for integrated urban modeling approaches, a Mixed Integer Linear Programing (MILP) optimization model representing both urban and energy system components was developed. Second, an interface based on parallel coordinates and georeferenced maps is proposed to effectively communicate the optimization results to decision makers, revealing tradeoffs and synergies between competing objectives. Interaction with the parallel coordinates charts further allows planners to steer consecutive optimization runs based on their preferences and experience. The framework is applied to an urban development project in Switzerland to demonstrate its usability and relevance.

Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support.

We explore how to rigorously evaluate multidimensional visualizations for their ability to support decision making. We first define multi-attribute choice tasks, a type of decision task commonly performed with such visualizations. We then identify which of the existing multidimensional visualizations are compatible with such tasks, and set out to evaluate three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consists in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants are then given multi-attribute choice tasks consisting of choosing holiday packages. We assess decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allow participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones. We finally discuss the limitations of our methods and directions for future work, such as the need for more sensitive metrics of decision support.

StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views.

Urban forms at human-scale, i.e., urban environments that individuals can sense (e.g., sight, smell, and touch) in their daily lives, can provide unprecedented insights on a variety of applications, such as urban planning and environment auditing. The analysis of urban forms can help planners develop high-quality urban spaces through evidence-based design. However, such analysis is complex because of the involvement of spatial, multi-scale (i.e., city, region, and street), and multivariate (e.g., greenery and sky ratios) natures of urban forms. In addition, current methods either lack quantitative measurements or are limited to a small area. The primary contribution of this work is the design of StreetVizor, an interactive visual analytics system that helps planners leverage their domain knowledge in exploring human-scale urban forms based on street view images. Our system presents two-stage visual exploration: 1) an AOI Explorer for the visual comparison of spatial distributions and quantitative measurements in two areas-of-interest (AOIs) at city- and region-scales; 2) and a Street Explorer with a novel parallel coordinate plot for the exploration of the fine-grained details of the urban forms at the street-scale. We integrate visualization techniques with machine learning models to facilitate the detection of street view patterns. We illustrate the applicability of our approach with case studies on the real-world datasets of four cities, i.e., Hong Kong, Singapore, Greater London and New York City. Interviews with domain experts demonstrate the effectiveness of our system in facilitating various analytical tasks.

Rate-Distortion Theory for Clustering in the Perceptual Space

How to extract relevant information from large data sets has become a main challenge in data visualization. Clustering techniques that classify data into groups according to similarity metrics are a suitable strategy to tackle this problem. Generally, these techniques are applied in the data space as an independent step previous to visualization. In this paper, we propose clustering on the perceptual space by maximizing the mutual information between the original data and the final visualization. With this purpose, we present a new information-theoretic framework based on the rate-distortion theory that allows us to achieve a maximally compressed data with a minimal signal distortion. Using this framework, we propose a methodology to design a visualization process that minimizes the information loss during the clustering process. Three application examples of the proposed methodology in different visualization techniques such as scatterplot, parallel coordinates, and summary trees are presented.

Visualized strategy for predicting buildings energy consumption during early design stage using parametric analysis

Recently, there has been a high level of demand for sustainable design of buildings construction. The catalysts for such demand are rising energy costs and increasing environmental concerns. Therefore, it is important to evaluate building energy consumption, which is the key problem of building energy saving. There is a need for a reliable energy simulation model to predict buildings energy consumption. In addition, designers suffer from the limitations of energy simulation tools during the early design stage that addresses buildings envelope and orientation. This study proposes a strategy for visualized parametric energy analysis of buildings during the conceptual design phase. It presents an energy-oriented workflow that accommodates the Egyptian context. A residential building model is developed by coupling parametric analysis and building energy modeling tools as a means of developing an energy consumption database. The parallel coordinate plot is used to visualize the database to allow decision makers to flexibly evaluate energy performance. The advantages of this paper stem from: 1) providing a new strategy for visualizing the predicting building energy consumption data; 2) facilitating the process of decision making for energy design alternatives during early stages; 3) integrating parametric modeling and energy simulation engine in one platform to come over the problem of interoperability; 4) developing a generic model with variant energy simulation parameters; and 5) presenting an automated and simplified approach to enable modelers to simulate and analyze building energy performance with a more timely, accurate and efficient process.

QUALITY ANALYSIS FOR THE VRP SOLUTIONS USING COMPUTER VISION TECHNIQUES

ABSTRACT The Vehicle Routing Problem (VRP) is a classical problem, and when the number of customers is very large, the task of finding the optimal solution can be extremely complex. It is still necessary to find an effective way to evaluate the quality of solutions when there is no known optimal solution. This work presents a suggestion to analyze the quality of vehicle routes, based only on their geometric properties. The proposed descriptors aim to be invariants in relation to the amount of customers, vehicles and the size of the covered area. Applying the methodology proposed in this work it is possible to obtain the route and, then, to evaluate the quality of solutions obtained using computer vision. Despite considering problems with different configurations for the number of customers, vehicles and service area, the results obtained with the experiments show that the proposal is useful for classifying the routes into good or bad classes. A visual analysis was performed using the Parallel Coordinates and Viz3D techniques and then a classification was performed by a Backpropagation Neural Network, which indicated an accuracy rate of 99.87%.

Visual Exploration of Large Multidimensional Data Using Parallel Coordinates on Big Data Infrastructure

The increase of data collection in various domains calls for an adaptation of methods of visualization to tackle magnitudes exceeding the number of available pixels on screens and challenging interactivity. This growth of datasets size has been supported by the advent of accessible and scalable storage and computing infrastructure. Similarly, visualization systems need perceptual and interactive scalability. We present a complete system, complying with the constraints of aforesaid environment, for visual exploration of large multidimensional data with parallel coordinates. Perceptual scalability is addressed with data abstraction while interactions rely on server-side data-intensive computation and hardware-accelerated rendering on the client-side. The system employs a hybrid computing method to accommodate pre-computing time or space constraints and achieves responsiveness for main parallel coordinates plot interaction tools on billions of records.

Adjustable general line coordinates for visual knowledge discovery in n-D data

Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in Visual Analytics, Machine Learning/Data Mining, and Multiobjective Pareto Optimization. While Parallel and Radial (Star) coordinates preserve all n-D data in two dimensions, they are not sufficient to address visualization challenges of all possible datasets such as occlusion. More such methods are needed. Recently, the concepts of lossless General Line Coordinates that generalize Parallel, Radial, Cartesian, and other coordinates were proposed with initial exploration and application of several subclasses of General Line Coordinates such as Collocated Paired Coordinates and Star Collocated Paired Coordinates. This article explores and enhances benefits of General Line Coordinates. It shows the ways to increase expressiveness of General Line Coordinates including decreasing occlusion and simplifying visual pattern while preserving all n-D data in two dimensions by adjusting General Line Coordinates for given n-D datasets. The adjustments include relocating, rescaling, and other transformations of General Line Coordinates. One of the major sources of benefits of General Line Coordinates relative to Parallel Coordinates is twice less number of point and lines in visual representation of each n-D points. This article demonstrates the benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine. The experimental part of the article presents the results of the experiment with about 70 participants on efficiency of visual pattern discovery using Star Collocated Paired Coordinates, Parallel, and Radial Coordinates. It shows advantages of visual discovery of n-D patterns using General Line Coordinates subclass Star Collocated Paired Coordinates with n = 160 dimensions.