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Organization component analysis: The method for extracting insights from the shape of cluster

Author
Mahdavi, K.; Labarta, J.; Giménez, J.
Type of activity
Presentation of work at congresses
Name of edition
International Joint Conference on Neural Networks 2021
Date of publication
2021
Presentation's date
2021-07-21
Book of congress proceedings
The International Joint Conference on Neural Networks, IJCNN 2021 program
Abstract
Clustering analysis is widely used to stratify data in the same cluster when they are similar according to specific metrics. The process of understanding and interpreting clusters is mostly intuitive. However, we observe each cluster has unique shape that comes out of metrics on data, which can represent the organization of categorized data mathematically. In this paper, we apply novel topological based method to study potentially complex high-dimensional categorized data by quantifying their sh...
Keywords
Cluster analysis, Self-organization map, Sequence similarity, Topological data analysis, Topology-preservation
Group of research
CAP - High Performace Computing Group

Participants