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arXiv Machine Learning By Hayato Suzuki, Shunnosuke Ikeda, Naoki Nishimura, Yuichi Takano

Interpretable clustering via optimal multi-way decision trees

Read the original on arXiv Machine Learning →

The paper introduces ICOMT, a framework for interpretable clustering using optimal multi-way decision trees. It proposes a new discretization technique based on one-dimensional K‑means, formulates a binary linear optimization problem to ensure tree optimality, and demonstrates superior clustering accuracy and shallow tree structures on four public datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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