Efficient k-means evaluation with silhouette score in sklearn

Question

I am running k-means clustering on ~1 million items (each represented as a ~100-feature vector). I have run the clustering for various k, and now want to evaluate the different results with the silhouette score implemented in sklearn. Attempting to run it with no sampling seems unfeasible and takes a prohibitively long time, so I assume I need to use sampling, i.e.:

metrics.silhouette_score(feature_matrix, cluster_labels, metric='euclidean',sample_size=???)

I don't have a good sense of what an appropriate sampling approach is, however. Is there a rule of thumb for what size sample to use given the size of my matrix? Is it better to take the largest sample my analysis machine can handle, or to take the average of more smaller samples?

I ask in large part because my preliminary test (with sample_size=10000) has produced some really really unintuitive results.

I'm also open to alternative, more scalable evaluation metrics.

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Editing to visualize the issue: The plot shows, for varying sample sizes, the silhouette score as a function of the number of clusters

What's not weird is that increasing sample size seems to reduce noise. What is weird, given that I have 1 million, very heterogenous vectors, that 2 or 3 is the "best" number of clusters. In other words, what's unintuitive is that I would find a more-or-less monotonic decreases in silhouette score as I increase the number of clusters.

Answer 1

kmeans converge to local minima. Starting positions plays a crucial role in optimal number of clusters. It would be a good idea often to reduce the noise and dimensions using PCA or any other dimension reduction techniques to proceed with kmeans.

Just to add for the sake of completeness. It might be a good idea to get optimal number of clusters by "partition around medoids". It is equivalent to using silhouette method.

Reason for the weird observations could be different starting points for different sized samples.

Having said all the above, it is important to evaluate clusterability of the dataset in hand. Tractable means is by Worst Pair ratio as discussed here Clusterability.

Answer 2

Other metrics

1. Elbow method: Compute the % variance explained for each K, and choose the K where the plot starts to level off. (a good description is here https://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set). Obviously if you have k == number of data points, you can explain 100% of the variance. The question is where do the improvements in variance explained start to level off.

2. Information theory: If you can calculate a likelihood for a given K, then you can use the AIC, AICc, or BIC (or any other information-theoretic approach). E.g. for the AICc, it just balances the increase in likelihood as you increase K with the increase in the number of parameters you need. In practice all you do is choose the K that minimises the AICc.

3. You may be able to get a feel for a roughly appropriate K by running alternative methods that give you back an estimate of the number of clusters, like DBSCAN. Though I haven't seen this approach used to estimate K, and it is likely inadvisable to rely on it like this. However, if DBSCAN also gave you a small number of clusters here, then there's likely something about your data that you might not be appreciating (i.e. not as many clusters are you're expecting).

How much to sample

It looks like you've answered this from your plot: no matter what your sampling you get the same pattern in silhouette score. So that patterns seems very robust to sampling assumptions.