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The history of the cluster heat map
 The American Statistician
, 2009
"... The cluster heat map is an ingenious display that simultaneously reveals row and column hierarchical cluster structure in a data matrix. It consists of a rectangular tiling with each tile shaded on a color scale to represent the value of the corresponding element of the data matrix. The rows (column ..."
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The cluster heat map is an ingenious display that simultaneously reveals row and column hierarchical cluster structure in a data matrix. It consists of a rectangular tiling with each tile shaded on a color scale to represent the value of the corresponding element of the data matrix. The rows (columns) of the tiling are ordered such that similar rows (columns) are near each other. On the vertical and horizontal margins of the tiling there are hierarchical cluster trees. This cluster heat map is a synthesis of several different graphic displays developed by statisticians over more than a century. We locate the earliest sources of this display in late 19th century publications. And we trace a diverse 20th century statistical literature that provided a foundation for this most widely used of all bioinformatics displays. 1
Dissimilarity Plots: A Visual Exploration Tool for Partitional Clustering
, 2009
"... For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between ..."
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For hierarchical clustering, dendrograms provide convenient and powerful visualization. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with several reordering steps based on seriation. Both methods, matrix shading and seriation, have been wellknown for a long time. However, only recent algorithmic improvements allow to use seriation for larger problems. Furthermore, seriation is used in a novel stepwise process (within each cluster and between clusters) which leads to a visualization technique that is independent of the dimensionality of the data. A big advantage is that it presents the structure between clusters and the microstructure within clusters in one concise plot. This not only allows for judging cluster quality but also makes misspecification of the number of clusters apparent. We give a detailed discussion of the construction of dissimilarity plots and demonstrate their usefulness with several examples.
Relativity and Resolution for High Dimensional Information Visualization with Generalized Association Plots (GAP)
, 2002
"... Generalized association plots (GAP) (Chen, 1996; 1999; 2002) is an information visualization environment for high dimensional data structure without dimension reduction. There is no limit for sample size and variable number. Three matrix maps for raw data matrix, object proximity matrix, and variabl ..."
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Generalized association plots (GAP) (Chen, 1996; 1999; 2002) is an information visualization environment for high dimensional data structure without dimension reduction. There is no limit for sample size and variable number. Three matrix maps for raw data matrix, object proximity matrix, and variable proximity matrix are created for visually extracting grouping structures for objects and variables and the interaction information between objectclusters and variablegroups. Seriation algorithms are developed to permute objects and variables such that rows and columns with similar profiles are arranged at closer positions. Categorical generalized association plots (cGAP) (Chen, 1999; Chen et al., 2002) is an extension of GAP adapted for visualizing high dimensional categorical data structure. Optimal scaling (multiple correspondence analysis) is applied to compute the proximity matrices for objects as well as for variables and to obtain colors for coding all categories in the raw data matrix. Relativity and resolution are two related critical issues in conducting efficient GAP and cGAP analyses. This article discusses possible solutions when standard procedures fail in generating satisfactory relativity and resolution for GAP and cGAP.
BioMed Central Research A protein interaction based model for schizophrenia study
, 2008
"... doi:10.1186/147121059S12S23 ..."
Mixing of Markov Chain Monte Carlo Simulations
, 2009
"... NOTICE: this is the author’s version of a work that was accepted for publication ..."
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NOTICE: this is the author’s version of a work that was accepted for publication
ISBN 8086732592 © MATFYZPRESS Matrix Visualization
"... Abstract. The paper deals with Matrix visualization method (MV). The importance of this technique rises with increasing volumes of analyzed data. The article involves introduction of MV accompanied by several figures to make the description more comprehensible. These pictures are plotted using the R ..."
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Abstract. The paper deals with Matrix visualization method (MV). The importance of this technique rises with increasing volumes of analyzed data. The article involves introduction of MV accompanied by several figures to make the description more comprehensible. These pictures are plotted using the R software, therefore the implementation of MV in this statistical software is briefly discussed. We focus on so called seriation problem at the end of the article, where some seriation criteria and algorithms are mentioned.
Computational and Interactive Visualization with a Focus on Topological Analysis, Dual Contouring and Waterresource Data Representation
, 2007
"... Increase in computing power has led to a substantial increase in the size of scientific and engineering data sets. Often, research in highdimensional spaces requires analysis of terabytes of data. This in turn has led to an increase in the demand for simplified representations of these large datase ..."
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Increase in computing power has led to a substantial increase in the size of scientific and engineering data sets. Often, research in highdimensional spaces requires analysis of terabytes of data. This in turn has led to an increase in the demand for simplified representations of these large datasets for effective analysis. Scientific visualization facilitates visual interpretation of massive data sets. Thus, visualization is driven by the needs of a broad spectrum of research areas. The first part of this dissertation describes use of topology to segment twodimensional tensor fields. The second part describes a ray intersection method to generate dual isosurfaces for trivariate, volumetric data. The third part describes an interactive visualization system for visualizing waterresource data. In the first two parts, we describe how topology can serve as foundation for two different areas: (a) In tensor field interpretation, to extract topology of the field based on different interpolation schemes to reduce complexity. (b) In volume visualization, to ensure topological correctness of isosurfaces, and using that as the underlying principle of the dualisosurfacing algorithm. In the third part, we present the visualization systems developed as a part of applicationdriven research concerned with management and planning of water resources. We describe two systems which support: (a) a global analysis of multiparameter timeseries data of different components of a large hydrological system, and (b) a localized statistical analysis of timeseries data of a single parameter in a specific part of a hydrological system.
HsingShih Wang4,5 and YingShiung Lee5 Open Access
"... Research article Molecular signature of clinical severity in recovering patients with ..."
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Research article Molecular signature of clinical severity in recovering patients with