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Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance

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Abstract

Clustering technology is a method for grouping data points into clusters containing a group of similar data points. In a real dataset such as microbiome data, the data points are presented as profiles or a probability distribution. These data points form the periphery of a cluster, making it difficult to identify the real clustering structure. In this study, we used density clustering on several distance measures to overcome this difficulty. Experiments using a real dataset indicated that the Manhattan distance is an appropriate distance measure for clustering analysis of microbiome data.

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Correspondence to Tingting He.

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Jiang, X., Hu, X. & He, T. Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance. Sci. China Inf. Sci. 59, 070104 (2016). https://doi.org/10.1007/s11432-016-5587-8

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Keywords

  • microbiome
  • information distance
  • data visualization
  • density clustering
  • microbial community