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Table 1 Summary of four particular clustering approaches.

From: Microbial community pattern detection in human body habitats via ensemble clustering framework

Clustering Approaches Characteristics Limitations on microbial pattern
Density-based clustering Clusters are defined as connected dense regions in the network True microbial community are not limited to densely connected structures; sparsely microbial structure still exists
Graph partition-based clustering Clusters are generated via graph partitioning techniques Partition based algorithms do not allow the overlaps between clusters. Therefore, they are unable to discover shared microbe among clusters, such as some species that could adapt in multi-environmental conditions like microbial mats and biofilms
Hierarchical clustering Clusters are built based on an agglomerative clustering model that shows relations between the members and groups Hierarchical structure is determined by local optimization criterion as such there is no global objective function, which might lead to small clusters with only part of similar samples
Distribution-based clustering Clusters are modelled using statistical distributions Statistical models of microbial communities are still unknown and need to be further explored