Name
Title | ||
|---|---|---|
Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance. |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11432-016-5587-8 | SCIENCE CHINA Information Sciences |
Keywords | Field | DocType |
microbiome, information distance, data visualization, density clustering, microbial community | k-medians clustering,Hierarchical clustering,Fuzzy clustering,Data mining,CURE data clustering algorithm,Clustering high-dimensional data,Correlation clustering,Cluster analysis,Mathematics,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
59 | 7 | 1869-1919 |
Citations | PageRank | References |
2 | 0.36 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingpeng Jiang | 1 | 34 | 20.30 |
| Xiaohua Hu | 2 | 2819 | 314.15 |
| Tingting He | 3 | 348 | 61.04 |