Journal of System Simulation
Abstract
Abstract: For the data in feature space, traditional clustering algorithm can take clustering analysis directly. High-dimensional spatial data cannot achieve intuitive and effective graphical visualization of clustering results in 2D plane. Graph data can clearly reflect the similarity relationship between objects. According to the distance of the data objects, the feature space data are modeled as graph data by iteration. Cluster analysis based on modularity is carried out on the modeling graph data. The two-dimensional visualization of non-spherical-shape distribution data cluster and result is achieved. The concept of credibility of the clustering result is proposed, and a method is proposed, which the Page Rank algorithm is used to calculate the reliability of clustering results.
Recommended Citation
Cheng, Yanyun; Bian, Huisong; and Bian, Changsheng
(2018)
"Clustering Method Based on Graph Data Model and Reliability Detection,"
Journal of System Simulation: Vol. 30:
Iss.
6, Article 13.
DOI: 10.16182/j.issn1004731x.joss.201806013
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss6/13
First Page
2102
Revised Date
2016-09-17
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201806013
Last Page
2109
CLC
TP391.9
Recommended Citation
Cheng Yanyun, Bian Huisong, Bian Changsheng. Clustering Method Based on Graph Data Model and Reliability Detection[J]. Journal of System Simulation, 2018, 30(6): 2102-2109.
DOI
10.16182/j.issn1004731x.joss.201806013
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