Identifying homogeneous subgroups of patients and important features: a topological machine learning approach

Identifying homogeneous subgroups of patients and important features: a topological machine learning approach

Background: This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph. Results: We present a pipeline to identify and summarise clusters based on statistically signifi...

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Bibliographic Details
Main Authors: Carr, E. (Author), Carrière, M. (Author), Chazal, F. (Author), Iniesta, R. (Author), Michel, B. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
adult
algorithm
Algorithms
article
bootstrapping
cluster analysis
Cluster Analysis
Clustering
Clustering algorithms
Clustering process
Complex data
data analysis
Data Analysis
Graph algorithms
human
Humans
Important features
licence
machine learning
Machine learning
Machine Learning
Machine learning approaches
Mixed data types
pipeline
Pipelines
Prior knowledge
Topological data analysis
Topological features
Topology
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