Question 1

What distinguishes grouping from classification?

Accepted Answer

Classification relies on pre‑labeled examples – each document has a known group. Grouping requires none of that: the algorithm finds clusters purely from semantic similarity. This uncovers hidden structure without any prior knowledge.

Question 2

Which technique works best for texts?

Accepted Answer

For exploration when the number of groups is unknown, HDBSCAN and BERTopic are top choices. HDBSCAN autonomously picks the count and marks outliers. BERTopic also supplies interpretable topic words. There is none better for zero‑knowledge discovery.

Question 3

What document count yields reliable clusters?

Accepted Answer

Larger datasets give steadier clusters. With 100–500 documents you can achieve stable groupings. For BERTopic, aim for at least 50 documents per cluster so c‑TF‑IDF produces meaningful terms. None of these guarantees apply below that threshold.

Question 4

How can we assess grouping quality without true labels?

Accepted Answer

Use internal indices: Silhouette Score (aim >0.3), Davies‑Bouldin Index (lower is better). Also examine the coherence of top words per cluster – how semantically connected they are. If none of the metrics agree, inspect manually.

Question 5

Does your pipeline handle Russian‑language texts?

Accepted Answer

Yes. We employ Russian‑specific embeddings like `cointegrated/rubert‑tiny2` and `sbert‑base‑ru‑mean‑tokens`. OpenAI’s `text‑embedding‑3-small` also works. In 80% of cases, we use the Sentence‑BERT ruBERT family. None of our clients have reported language issues.

Unsupervised Document Grouping: Workflow, Methods, Practical Examples

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