Hypergraph Clustering Based on Intra-class Scatter Matrix for Mining Higher-order Microbial Module

Yu, Limin; Shen, Xin; Jiang, Xingpeng; Yang, Jincai; Yu-juan, Yang; Zhong, Duo

doi:10.1109/bibm47256.2019.8983390

Cited by 2 publications

(4 citation statements)

References 13 publications

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Section: Results and Analysismentioning

confidence: 99%

“…In the previous work, we used to find the maximum degree of modularity to determine the number of clusters before the premise of clustering. 24 In this paper, we studied the hypergraph clustering problem from the perspective of game theory to analyze the high-order module analysis of microorganisms, which can automatically generate clusters without specifying the number of clusters.…”

Section: Hypergraph Clustering Based On Game Theorymentioning

confidence: 99%

“…In this paper, the joint entropy, total correlation and modularity are taken as the indexes to evaluate the clustering performance of the algorithm, the hyperspectral clustering based on game theory and intra-class scatter matrix (HCGI) and hypergraph clustering based on intra-class scatter matrix (HCIS). 24 They were applied to cluster analysis and comparison of microorganisms in 18 parts of human body. Based on the clustering results, the JE and TC of each cluster were calculated.…”

Section: Comparison Of Algorithmsmentioning

confidence: 99%

“…We selected the mid vagina where the clustering effect did not change much after the addition of intra-class scatter matrix and intestines tract where HCIS did not work well in to compare and analyze in the previous work. 24 Tables 2 and 3 show that cluster analysis and comparison of mid vagina and intestines tract based on type 1. We can see that the two methods end up with different clustering numbers on the same body part.…”

Section: Comparison Of Algorithmsmentioning

confidence: 99%

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Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module

Shen

Yang

et al. 2020

Evol Bioinform Online

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Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI .

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Section: Results and Analysismentioning

confidence: 99%

Section: Hypergraph Clustering Based On Game Theorymentioning

confidence: 99%

Section: Comparison Of Algorithmsmentioning

confidence: 99%

Section: Comparison Of Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations