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Related Concept Videos

Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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pV-Diagrams01:18

pV-Diagrams

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The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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Pie Chart01:04

Pie Chart

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A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
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Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Modified Boxplots00:57

Modified Boxplots

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
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A Five-Level Design Framework for Bicluster Visualizations.

Maoyuan Sun, Chris North, Naren Ramakrishnan

    IEEE Transactions on Visualization and Computer Graphics
    |September 11, 2015
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    Summary
    This summary is machine-generated.

    This study introduces a five-level design framework to improve bicluster visualizations. This framework enhances the exploration of coordinated relationships within large datasets for better data analysis and hypothesis generation.

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    Area of Science:

    • Data Visualization
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Analysts require methods to identify coordinated relationships in large datasets for sensemaking.
    • Biclustering offers a way to group related data points, but effective visualization remains a challenge.
    • Lack of systematic design guidelines hinders the creation of usable bicluster visualizations.

    Purpose of the Study:

    • To present a novel five-level design framework for effective bicluster visualizations.
    • To survey existing design considerations and applications relevant to bicluster visualization.
    • To support analysts in exploring coordinated relationships and leveraging domain knowledge.

    Main Methods:

    • Developed a five-level design framework for bicluster visualization.
    • Conducted a survey of state-of-the-art design considerations and applications.
    • Summarized the pros and cons of design options for user tasks.

    Main Results:

    • The proposed framework addresses the need for systematic design guidelines in bicluster visualization.
    • Identified and categorized various design options to enhance perceptibility and interactivity.
    • Provided a structured approach to support different levels of relationship exploration.

    Conclusions:

    • Effective bicluster visualizations are crucial for exploring coordinated relationships in large datasets.
    • The five-level design framework offers a systematic approach to designing and evaluating bicluster visualizations.
    • Future research should focus on integrating bicluster visualizations into broader visual analytics tools.