Topological Clustering
Topological clustering analyzes the shape and structure of data, aiming to group similar data points based on their topological features rather than solely on their Euclidean distances. Current research focuses on applying this approach to diverse fields, employing methods like persistent homology, graph neural networks, and adaptive resonance theory to analyze brain connectivity, neuron classification, and even large language model knowledge representation. These advancements offer improved accuracy in tasks such as disease diagnosis (e.g., MCI subtypes), network analysis, and potentially more robust and interpretable machine learning models.
Papers
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