ERICA: Quantifying Replicability of Cluster Analysis
Despite being ubiquitous in science, clustering lacks a unified framework for quantitatively evaluating the replicability of its results. We present evaluating replicability via iterative clustering assignments (ERICA), a method for determining whether clusters can be identified reproducibly in a dataset. The pipeline computes a statistic that determines whether reproducible cluster structure is present in a dataset. Quantitative visualization methods are also introduced to characterize similarities between clusters and identify observations that may represent outliers or unstable assignments. Experiments on synthetic datasets demonstrate that ERICA successfully identifies reproducible cl...
| Authors | Siamak K. Sorooshyari, Manuel A. Rivas, Robert Tibshirani |
|---|---|
| Primary category | stat.ML |
| Published | 29 May 2026 |
| Last updated at source | 28 June 2026 |
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