If we use agglomerative clustering on the same dataset multiple times, it's possible we get different clusters each time.

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BlackTom AI · Accepted Answer

Here's the statement to evaluate: 'If we use agglomerative clustering on the same dataset multiple times, it's possible we get different clusters each time.'

First, consider what agglomerative clustering involves. It is a hierarchical clustering method that starts with each data point as its own cluster and successively merges the closest pair of clusters according to a chosen linkage criterion (e.g., single, complete, average, Ward) and a distance metric.

In a deterministic implementation with fixed data, fixed distance function, and fixed linkage rule, the process is deterministic. This means that, given the same inputs and the same tie-breaking rules, you should obtain the same clustering results on every run.

However, there are realistic scenarios where you could see different clusters across runs. One cause is the choice of linkage criterion or distance metric being altered between runs, which changes how proximity and merging decisions are made.

Another common source of variability is tie-breaking nondeterminism. When two candidate merges have identical merge costs, the algorithm must choose one arbitrarily. If the implementation handles ties in a non-deterministic way (or if the order of data points influences the tie resolution), you can obtain different clusterings on different runs.

Additionally, if the dataset undergoes any preprocessing steps that introduce randomness (for example, random sampling, shuffling before processing that affects internal ordering, or stochastic data augmentation), the resulting clustering can vary.

There is also the practical possibility that parallelization or numerical precision differences across runs might lead to slight differences in merge decisions in some software implementations, again yielding different results.

To summarize, while the core algorithm is deterministic under fixed conditions, variability can arise from: changing linkage/distance settings, nondeterministic tie-breaking, or any randomness in preprocessing or computation. Consequently, the claim is not universally true; it is context-dependent.

Note: The provided answer options are not listed here, but the general conclusion aligns with common understanding: under strictly identical settings, agglomerative clustering should reproduce the same clustering; differences arise if any of the factors above differ between runs.

MISM2510.13156.202610

If we use agglomerative clustering on the same dataset multiple times, it's possible we get different clusters each time.

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