A Data Binding Method for Parallel Coordinate System View Clustering of Force-Directed Segmented Bones

A data binding and coordinate system technology, which is applied in the directions of instruments, computing, character and pattern recognition, etc., can solve the problems of data line clutter interference, visual cognitive deviation, etc., and achieve the effect of reducing overlapping and interlacing and improving visual cognitive effect

Inactive Publication Date: 2019-11-08
BEIJING FORESTRY UNIVERSITY
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Problems solved by technology

[0009] In order to solve the data visualization fields such as 3D modeling design, data mining, business decision-making, market research, user research, etc., the clustering data analysis of the parallel coordinate system widely uses the binding method to solve the problem of data line clutter interference
The patent of the present invention proposes a data binding method of view clustering in parallel coordinate system based on force-guided segmented bones. Through the innovative design of the binding method, the line layout after binding is improved, and the existing parallel coordinate system clustering is solved. The problem of visual cognition bias that may be caused by the distribution characteristics of the data in the class binding method

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  • A Data Binding Method for Parallel Coordinate System View Clustering of Force-Directed Segmented Bones
  • A Data Binding Method for Parallel Coordinate System View Clustering of Force-Directed Segmented Bones
  • A Data Binding Method for Parallel Coordinate System View Clustering of Force-Directed Segmented Bones

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Embodiment 1

[0093] like Figure 7 As shown, assume that the data in the view of the parallel coordinate system is clustered by the K-MEANS (K-means) method to obtain 4 clusters, among which, the number of samples in cluster 1 is 7, and the number of samples in cluster 2 is 55. The sample size of cluster 3 is 72, and the sample size of cluster 4 is 65. Use the traditional binding method and the binding method of the present invention to draw respectively, and compare the results.

[0094] 1) Traditional binding method:

[0095] Step 1: According to the sample data contained in each cluster, calculate the center of each cluster as the benchmark for the clustering of the cluster.

[0096] Step 2: Calculate the distance H between clusters according to the height of the coordinate axis and the number N of clusters.

[0097] The calculation model is: H=Height / N

[0098] Step 3: Arrange the center (mean line) positions of the four clusters evenly within the height range of the coordinate axi...

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Abstract

A data binding method for parallel coordinate system view clustering of force-directed segmented bones. Through the innovative design of the binding method, the line layout after binding is improved, and the possible problems in the existing parallel coordinate system clustering binding method are solved. The problem of visual cognitive bias caused by the distribution characteristics of the data.

Description

technical field [0001] The invention relates to the field of high-dimensional data visualization rendering, in particular to a parallel coordinate system view clustering data binding method based on force-guided segmented bones. Background technique [0002] 1) Big data visualization: Data visualization is an important part of the field of computer graphics. Through the design of visualization methods, the data is drawn and presented in the form of two-dimensional or three-dimensional graphics, and the visual cognition channel is used to help users complete the understanding of information and analyze. [0003] 2) Parallel coordinate system view: (Parallel Coordinates Plot, PCP) is one of the main ways to visualize high-dimensional big data. It can map multi-attribute high-dimensional data on a two-dimensional plane with multiple coordinate axes arranged in parallel to help users perform data analysis. One of the basic problems of PCP view is how to solve the visual clutte...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 巫滨曹卫群李苏南杨波
Owner BEIJING FORESTRY UNIVERSITY
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