#unsupervised

Dimensionality reduction is the process of reducing the number of features (dimensions) in a dataset while preserving as much relevant information as possible.

K-means is an unsupervised machine learning algorithm used for partitioning a dataset into K distinct, non-overlapping subgroups (clusters).

Clustering metrics are quantitative measures used to evaluate the performance and quality of clustering algorithms.

BERTopic is a modular topic modeling pipeline.

Latent Dirichlet Allocation (Blei, Ng, Jordan, 2003) is the canonical probabilistic topic model.

A survey of the main topic modeling methods, ordered roughly by historical development (matrix factorization → probabilistic generative models → neural → embedding-based).

Topic modeling is an unsupervised technique for discovering abstract themes in a document collection, where a document is whatever unit of text the project treats as one (article, review, tweet, paragraph, support ticket).