Visualizing the relationship of multi dimensional data in 2d relationship space
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Mapping Between 2D Data Space and 2D Relationship Space. Lei Wang and Arie Kaufman Computer Science Department, Stony Brook University. Relationship Space. Possible Applications: Data Analysis : Visualize the relationship between data and features.

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Visualizing the Relationship of Multi-dimensional Data in 2D Relationship Space

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Visualizing the relationship of multi dimensional data in 2d relationship space

Mapping Between 2D Data Space and 2D RelationshipSpace

Lei Wang and Arie Kaufman

  • Computer Science Department, Stony Brook University

Relationship Space

  • Possible Applications:

    • Data Analysis: Visualize the relationship between data and features.

    • Data Classification: Classify the data according to the relationship.

    • Data Feature Detection: Extract features with special relation properties.

    • Data Visualization: Visualize the data based on the relationship.

    • Data Registration: Register the data with similar relationship in two data sets.

The properties of the relationship space depend on the different relations.

Low

Visualizing the Relationship of Multi-dimensional Data in 2D Relationship Space

Number of data

Volume Data Analysis

Volume Data Visualization

The relationship space is calculated from the features of the 3D volume data, using feature distance as a relation.

The relationship space is invariant under noise, bias, and distortion. It shows the relationship of the data.

The relationship of the space is used for transfer function designation.

High

Data

Relationship

High

Low

Strength of relation

The intensity shows the number of data under each relation strength.

The spatial relationship of the grids encodes the relationship of data points

Bonsai

Lobster

Volume Data Classification

The classification is obtained by classifying the grids in the relationship space.

Volume

Feature

Relationship

Noise

Bias

Distortion

  • We use 2D data for the convenience of display. The algorithm and properties are same in high-dimensional data space.


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