An interpretable Good--Turing restart criterion for k-means++

The k-means++ algorithm is commonly restarted multiple times to avoid poor local optima, yet the number of restarts is almost always chosen arbitrarily and applied uniformly regardless of data set difficulty.

This undermines any comparison relying on such a choice and wastes computation on easy data sets while potentially under-serving hard ones.

We introduce GTRC, a restart criterion combining a Good-Turing estimate, a proven unconditional bound, and a confidence-based bound on the probability that a further restart would improve on the current result, stopping once this probability falls below a user-specified tolerance $\varepsilon$.

Across 36 data sets, GTRC reached clustering quality competitive with well-chosen fixed restart counts, while the number of restarts used varied considerably and appropriately with data set difficulty, governed by an interpretable, data-dependent signal rather than a fixed rule.

GTRC offers a principled and reportable alternative to fixing the number of $k$-means++ restarts in advance.

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