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

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成果类型:
期刊论文
作者:
Jiang, Xingpeng(蒋兴鹏);Hu, Xiaohua;He, Tingting*何婷婷
通讯作者:
He, Tingting(何婷婷
作者机构:
[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构:
[He, Tingting] C
Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
microbiome;information distance;data visualization;density clustering;microbial community
关键词(中文):
聚类技术;距离度量;类结构;微生物;曼哈顿;组数;密度;识别
期刊:
中国科学:信息科学(英文版)
ISSN:
1674-733X
年:
2016
卷:
59
期:
7
页码:
070104-1-070104-7
基金类别:
This research was supported by National Natural Science Foundation of China (Grant No. 61532008), International Cooperation Project of Hubei Province (Grant No. 2014BHE0017), and Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of ME (Grant No. CCNU16KFY04, CCNU 14A02008).
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
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 ...

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