Minimum Information Trees for High Dimensional Data Visualization in Clustering

Visualizing the qualitative outcomes of clustering algorithms in high-dimensional spaces remains a persistent challenge in data analysis and machine learning. Traditional dimensionality reduction techniques often distort the underlying structure of clusters or fail to provide interpretable represent...

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Bibliographic Details

Published in:	IEEE Access
Main Author:	Alexandre L. M. Levada
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Subjects:	Clustering data visualization Fisher information minimum spanning tree Markov random fields Potts model
Online Access:	https://ieeexplore.ieee.org/document/11141445/