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FuzzyRadar: visualization for understanding fuzzy clusters

JOURNAL OF VISUALIZATION 2019年第5期22卷 913-926页

作者： Zhou, Fangfang Bai, Bing Wu, Yitao Chen, Minghui Zhong, Zengsheng Zhu, Rongchen Chen, Yi Zhao, Ying Cent S Univ Sch Comp Sci & Engn Changsha Hunan Peoples R China Cent S Univ Sch Automat Changsha Hunan Peoples R China Beijing Technol & Business Univ Beijing Key Lab Big Data Technol Food Safety Beijing Peoples R China

Fuzzy clustering assigns a membership degree (MD) on a datum to a cluster, which reflects real-world clustering scenarios but increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that multidimensional visualization techniques are beneficial to fuzzy clusters analysis. However, empirically, no single existing visualization technique can support most analytical tasks featured by fuzzy clustering. This work proposes a new visualization called FuzzyRadar for understanding fuzzy clusters. Its basic idea is to combine the advantages of radial coordinate visualization (Radviz), which specializes in data-oriented analytical tasks, and parallel coordinate plot (PCP), which performs well in cluster-oriented analytical tasks. First, we adopt a compact and compounded layout to integrate Radviz and PCP into one visualization view. Then, we introduce a strip-edge-bundling method to reduce the visual cluster caused by PCP polylines and a histogram embedding method to facilitate the recognition of MD distribution. We also provide a group of additional visual encodings and a set of lightweight interactions. Finally, we use a case study to demonstrate the usability of FuzzyRadar and conduct a controlled quantitative evaluation to compare the performance of FuzzyRadar, Radviz, PCP, and scatterplot matrix. Result shows that FuzzyRadar supports all the seven examined analytical tasks well and presents a significant capability improvement compared with Radviz and PCP.

关键词： Visualization Visual analysis Fuzzy clustering Radviz parallel coordinate plot

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Testing of uniformity of seven Lathyrus species using Bennett's and Miller's methods

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EUPHYTICA 2016年第1期208卷 123-128页

作者： Zawieja, Bogna Rybinski, Wojciech Nowosad, Kamila Piesik, Dariusz Bocianowski, Jan Poznan Univ Life Sci Dept Math & Stat Methods Wojska Polskiego 28 PL-60637 Poznan Poland Polish Acad Sci Inst Plant Genet Poznan Poland Wroclaw Univ Environm & Life Sci Dept Genet Plant Breeding & Seed Prod Wroclaw Poland UTP Univ Sci & Technol Dept Entomol & Mol Phytopathol Bydgoszcz Poland

In this paper we focused on uniformity of seven Lathyrus species: Lathyrus aphaca, L. cicero, L. clymenum, L. hirsutus, L. ochrus, L. sativus and L. tingitanus. Two statistical methods, the Bennett's and the Miller's were compared. In both methods, coefficient of variation for evaluation of the uniformity of crop varieties was used. Four quantitative traits were analyzed: length of the pod, width of the pod, weight of seeds from the pod and 100 seed weight. The results of the study demonstrated that, the Bennett's method is less restrictive (lesser species were found to be uniform in analyzed traits) compared to the Miller's one.

关键词： Coefficient of variation Lathyrus species parallel coordinate plot Quantitative traits Uniformity

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A parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data

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TECHNOMETRICS 2013年第4期55卷 513-523页

作者： Chen, Wei-Chen Ostrouchov, George Pugmire, David Prabhat Wehner, Michael Univ Tennessee Dept Ecol & Evolutionary Biol Knoxville TN 37996 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA Univ Calif Berkeley Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

We develop a parallel expectation-maximization (EM) algorithm for multivariate Gaussian mixture models and use it to perform model-based clustering of a large climate dataset. Three variants of the EM algorithm are reformulated in parallel and a new variant that is faster is presented. All are implemented using the single program, multiple data programming model, which is able to take advantage of the combined collective memory of large distributed computer architectures to process larger datasets. Displays of the estimated mixture model rather than the data allow us to explore multivariate relationships in a way that scales to arbitrary size data. We study the performance of our methodology on simulated data and apply our methodology to a high-resolution climate dataset produced by the community atmosphere model (CAM5). This article has supplementary material online.

关键词： parallel computing parallel coordinate plot Spatial time series Unsupervised learning

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Yet another visualization of the Iris data: The bridge plot

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Model Assisted Statistics and Applications 2011年第1期6卷 11-16页

作者： Kozak, Marcin Department of Experimental Design and Bioinformatics Warsaw University of Life Sciences 02-776 Warsaw Nowoursynowska 159 Poland

The bridge plot is used to visualize the famous Iris data. With the help of trellis system to condition the data on species, the plot presents four iris flower traits in their original units within a single panel. Such a plot can be employed when among four variables there are two groups each consisting of two variables having the same and comparable units. © 2011 - IOS Press and the authors. All rights reserved.

关键词： Original units parallel coordinate plot scatterplot matrix

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A computational framework for finding parameter sets associated with chaotic dynamics

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In Silico Biology 2020年第1-2期14卷 41-51页

作者： Koshy-Chenthittayil, S. Dimitrova, E. Jenkins, E.W. Dean, B.C. Center for Quantitative Medicine UConn Health Farmington United States Department of Mathematics California Polytechnic State University San Luis Obispo United States School of Mathematical and Statistical Sciences Clemson University Clemson United States School of Computing Clemson University Clemson United States

Many biological ecosystems exhibit chaotic behavior, demonstrated either analytically using parameter choices in an associated dynamical systems model or empirically through analysis of experimental data. In this paper, we use existing software tools (COPASI, R) to explore dynamical systems and uncover regions with positive Lyapunov exponents where thus chaos exists. We evaluate the ability of the software's optimization algorithms to find these positive values with several dynamical systems used to model biological populations. The algorithms have been able to identify parameter sets which lead to positive Lyapunov exponents, even when those exponents lie in regions with small support. For one of the examined systems, we observed that positive Lyapunov exponents were not uncovered when executing a search over the parameter space with small spacings between values of the independent variables. © 2020 - The authors. Published by IOS Press.

关键词： Chaotic dynamics genetic algorithm Lyapunov exponent parallel coordinate plot population dynamics

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Optimization of principal component analysis in feature extraction

Optimization of principal component analysis in feature extr...

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IEEE International Conference on Mechatronics and Automation

作者： Gao Haibo Hong Wenxue Cui Jianxin Xu Yonghong Univ Yanshan Dept Biomed Engn Quihuangdao Hebei Peoples R China

ISBN: (纸本)9781424408276

A novel method for optimising the principal component analysis in feature extraction is proposed, which makes use of parallel coordinate plot for graphical presentation of multivariate information. The objectivity and automatization of above manual observation and filtering process is realized by algorithm. In supervised multivariate information classification, before feature extraction on principal component analysis, filtering the variable that has bigger variance and has little effect on classification by observing the parallel coordinate plot of the multivariate data, the eigenvector from principal component analysis will be more in favor of classification. We achieved better performance when using this method to test the vegetable oil data. We believe that this method can be used in many other feature extraction methods, and will obtain better performance than them.

关键词： multivariate information classification principal component analysis feature extraction parallel coordinate plot sorted overlap coefficient

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A Topologically Consistent Visualization of High Dimensional Pareto-front for Multi-Criteria Decision Making 8

A Topologically Consistent Visualization of High Dimensional...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Talukder, A. K. M. Khaled Ahsan Deb, Kalyanmoy Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9781538692769

There are a good number of different algorithms to solve multi- and many-objective optimization problems and the final outcome of these algorithms is a set of trade-off solutions that are expected to span the entire Pareto-front. Visualization of a Pareto-front is vital for an initial decision-making task, as it provides a number of useful information, such as closeness of one solution to another, trade-off among conflicting objectives, localized shape of the Pareto-front visa-vis the entire front, and others. Two and three-dimensional Pareto-fronts are trivial to visualize and allow all the above analysis to be done comprehensively. However, for four or more objectives, visualization for extracting above decision-making information gets challenging and new and innovative methods are long overdue. Not only does a trivial visualization becomes difficult, the number of points needed to represent a high-erdimensional front increase exponentially. The existing high-dimensional visualization techniques, such as parallel coordinate plots, scatter plots, RadVis, etc., do not offer a clear and natural view of the Pareto-front in terms of trade-off and other vital localized information needed for a convenient decision-making task. In this paper, we propose a novel way to map a high-dimensional Pareto-front in two and three dimensions. The proposed method tries to capture some of the basic topological properties of the Pareto points and retain them in the mapped lower dimensional space. Therefore, the proposed method can produce faithful representation of the topological primitives of the high-dimensional data points in terms of the basic shape (and structure) of the Pareto-front, its boundary, and visual classification of the relative trade-offs of the solutions. As a proof-of-principle demonstration, we apply our proposed palette visualization method to a few problems.

关键词： Many-objective Optimization High-dimensional Pareto-front Visualization RadVis parallel coordinate plot Decision Making

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Quantitative Exploration of Large Medieval Manuscripts Data for the Codicological Research 6

Quantitative Exploration of Large Medieval Manuscripts Data ...

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6th IEEE Symposium on Large Data Analysis and Visualization (LDAV)

作者： Chandna, Swati Rindone, Francesca Dachsbacher, Carsten Stotzka, Rainer Karlsruhe Inst Technol Inst Data Proc & Elect Karlsruhe Germany Karlsruhe Inst Technol Inst Visualizat & Data Anal Karlsruhe Germany

ISBN: (纸本)9781509056590

Quantitative exploration is gaining in importance for the analysis of the digitized medieval manuscripts. While codicologists can collect such massive amounts of heterogeneous datasets digitized in high-resolution, they still lack efficient and intuitive means to explore data and answer domain-specific research questions. A new approach is needed to enable codicologists with the quantitative exploration of large of data. This paper presents a concept of a fully integrated system to enable a quantitative exploration of various layout features and their uncertainties over a large collection of medieval manuscripts. It is composed of three main components: data handling that ingests large amounts of data into a data repository, feature extraction that extracts various layout features of the manuscripts and quantitative exploration for visual analysis. In addition to this, we introduce new visualization approaches, i.e. the superimposition plot and the manuscript montage plot in combination with the parallel coordinate plot to explore structural layout features of 170,000 manuscript pages with more than 2.5 million measurements of these layout features. Our approach supports codicologists to see the overall structure of the manuscript at a single glimpse, explore heterogeneous layout features and convey uncertainties. We demonstrate typical use-case scenarios of our collaborators in codicological research where our system has enabled them to answer domain-specific questions for analysis of the medieval manuscripts data for the first time.

关键词： medieval manuscripts visualization codicology data handling feature extraction parallel coordinate plot quantitative exploration uncertainties knowledge discovery

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Research on optimization of multivariate information feature extraction based on graphical presentation

Research on optimization of multivariate information feature...

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8th International Conference on Electronic Measurement and Instruments

作者： Cui, Jianxin Hong, Wenxue Gao, Haibo Department of Biomedical Engineering University of Yanshan Qinhuangdao China

ISBN: (纸本)1424411351

A novel method for optimizing the principal component analysis in feature extraction is proposed, which making use of parallel coordinate plot for graphical presentation of multivariate information. In supervised multivariate information classification, before feature extraction on principal component analysis, filtering the variable that has bigger variance and has little effect on classification by observing the parallel coordinate plot of the multivariate data, the eigenvector from principal component analysis will be more in favor of classification. We achieved better performance when using this method to test the vegetable oil data. We believe that this method can be used in many other feat-tire extraction methods, and will obtain better performance than them.

关键词： principal component analysis parallel coordinate plot multivariate information feature extraction

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CellProfiler Analyst: data exploration and analysis software for complex image-based screens

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BMC BIOINFORMATICS 2008年第1期9卷 482-482页

作者： Jones, Thouis R. Kang, In Han Wheeler, Douglas B. Lindquist, Robert A. Papallo, Adam Sabatini, David M. Golland, Polina Carpenter, Anne E. MIT & Harvard Broad Inst Cambridge MA 02142 USA Whitehead Inst Biomed Res Cambridge MA 02142 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02142 USA MIT Dept Biol Cambridge MA 02142 USA

Background: Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment. Results: Here, we describe CellProfiler Analyst, open-source software for the interactive exploration and analysis of multidimensional data, particularly data from high-throughput, image-based experiments. Conclusion: The system enables interactive data exploration for image-based screens and automated scoring of complex phenotypes that require combinations of multiple measured features per cell.

关键词： Additional Data File Cell Cycle Distribution Sequential Gating parallel coordinate plot Telophase Nucleus

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