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Related Concept Videos

Histogram01:05

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
Probability Histograms01:17

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
Bar Graph01:07

Bar Graph

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...
Ogive Graph01:07

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this type...
What is a Frequency Distribution00:51

What is a Frequency Distribution

A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

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Angular histograms: frequency-based visualizations for large, high dimensional data.

Zhao Geng1, ZhenMin Peng, Robert S Laramee

  • 1Visual Computing Group, Swansea University, UK. cszg@swansea.ac.uk

IEEE Transactions on Visualization and Computer Graphics
|October 29, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces angular histograms and attribute curves to visualize large, high-dimensional data, overcoming parallel coordinates

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Area of Science:

  • Data Visualization
  • High-Dimensional Data Analysis
  • Computer Science

Background:

  • Parallel coordinates are a common multivariate data visualization technique.
  • Rendering large datasets with parallel coordinates leads to overplotting and clutter.
  • Overplotting hinders visual information seeking due to cluttered overviews and slow updates.

Purpose of the Study:

  • To propose novel frequency-based visualization techniques for large, high-dimensional data.
  • To address the limitations of traditional parallel coordinates in handling large datasets.
  • To introduce angular histograms and attribute curves as solutions to overplotting and clutter.

Main Methods:

  • Developed angular histograms and attribute curves as frequency-based visualization methods.
  • These techniques visualize density and slopes of polylines in high-dimensional data.
  • Compared the proposed methods with existing popular frequency-based algorithms.

Main Results:

  • Angular histograms and attribute curves effectively convey data density and slopes.
  • The new methods enable intuitive exploration of clustering, linear correlations, and outliers.
  • Demonstrated effectiveness on diverse datasets, including real-world high-dimensional biological data.

Conclusions:

  • Angular histograms and attribute curves provide a viable alternative to parallel coordinates for large datasets.
  • These techniques mitigate overplotting and clutter issues inherent in traditional parallel coordinates.
  • The proposed methods facilitate efficient exploration of complex, high-dimensional data.