Algorithmically Aligned Neural Agglomerative Tree Construction
Read the original on arXiv Machine Learning →The paper introduces NN‑linkage, a neural network model that learns task‑specific merge rules for hierarchical clustering while preserving the recursive structure and efficiency of classical linkage algorithms. It aligns with the Lance‑Williams recurrence, enabling it to approximate any continuous linkage function and even implement symmetric constant‑coefficient LW recurrences exactly. Experiments on clock‑tree routing and phylogenetic reconstruction show that NN‑linkage outperforms both traditional linkage methods and other neural approaches, extending efficient hierarchical clustering to new scientific and engineering tasks.
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