Paper Info
Title
Hypergraph Clustering Based on Intra-class Scatter Matrix for Mining Higher-order Microbial Module
Abstract
Microbial ecosystems are complex, by analyzing co-occurrence modules of microbial communities, we can better understand the conditions of microbial interactions in each environment, and help understand the interaction patterns that maintain the stability of microbial communities. Imbalances in human microbiome are closely related to human disease. Previous modular clustering analysis was based only on the relationship between paired microorganisms. In this paper, we propose calculating the logical relationship between microbial triplet in human body by information entropy and construct a hypergraph based on the triplet network. Based on the hypergraph clustering, we proposed a novel hypergraph clustering algorithm based on intra-class scatter matrix (HCIS) to reconstruct hyperedge similarity, and selected the optimal cluster number by maximizing modularity to analyze higher-order module of microorganisms. The clustering results verify the effectiveness and feasibility of HCIS algorithm for higher-order microbial module analysis.
Year
DOI
Venue
2019
10.1109/BIBM47256.2019.8983390
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
Higher-order microbial module,Intra-class scatter matrix,Hypergraph clustering
Data mining,Computer science,Hypergraph,Determining the number of clusters in a data set,Artificial intelligence,Modular design,Hypergraph clustering,Cluster analysis,Entropy (information theory),Machine learning,Modularity,Scatter matrix
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-7281-1868-0
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
LiminYu100.34
Xianjun Shen22412.95
XingpengJiang300.34
Jin Cai Yang442.51
Yujuan Yang500.34
Duo Zhong601.69