Journal article
Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance
Science China. Information sciences, v 59(7), pp 40-46
01 Jul 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
Metrics
Details
- Title
- Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance
- Creators
- Xingpeng Jiang - Central China Normal UniversityXiaohua Hu - Central China Normal UniversityTingting He - Central China Normal University
- Publication Details
- Science China. Information sciences, v 59(7), pp 40-46
- Publisher
- Science Press
- Number of pages
- 7
- Grant note
- 2014BHE0017 / International Cooperation Project of Hubei Province CCNU16KFY04; CCNU14A02008 / Self-determined Research Funds of CCNU from the Colleges' Basic Research and Operation of MOE 61532008 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000380685000004
- Scopus ID
- 2-s2.0-84975110924
- Other Identifier
- 991019167633904721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic