This project implements the Gibbs sampling algorithm for a Dirichlet Mixture Model of Yin and Wang 2014 for the clustering of short text documents. Some advantages of this algorithm:

• It requires only an upper bound K on the number of clusters
• With good parameter selection, the model converges quickly
• Space efficient and scalable

This project is an easy to read reference implementation of GSDMM -- I don't plan to maintain it unless there is demand. I am however actively maintaining the much faster Rust version of GSDMM here.

In their paper, the authors introduce a simple conceptual model for explaining the GSDMM called the Movie Group Process.

Imagine a professor is leading a film class. At the start of the class, the students are randomly assigned to K tables. Before class begins, the students make lists of their favorite films. The professor repeatedly reads the class role. Each time the student's name is called, the student must select a new table satisfying one or both of the following conditions:

• The new table has more students than the current table.
• The new table has students with similar lists of favorite movies.

By following these steps consistently, we might expect that the students eventually arrive at an "optimal" table configuration.

To use a Movie Group Process to cluster short texts, first initialize a MovieGroupProcess:

from gsdmm import MovieGroupProcess
mgp = MovieGroupProcess(K=8, alpha=0.1, beta=0.1, n_iters=30)

It's important to always choose K to be larger than the number of clusters you expect exist in your data, as the algorithm can never return more than K clusters.

To fit the model:

y = mgp.fit(docs)

Each doc in docs must be a unique list of tokens found in your short text document. This implementation does not support counting tokens with multiplicity (which generally has little value in short text documents).