MLA 011 Practical Clustering Tools

Machine Learning Guide

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Primary clustering tools for practical applications include K-means using scikit-learn or Faiss, agglomerative clustering leveraging cosine similarity with scikit-learn, and density-based methods like DBSCAN or HDBSCAN. For determining the optimal number of clusters, silhouette score is generally preferred over inertia-based visual heuristics, and it natively supports pre-computed distance matrices.

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Notes and resources at ocdevel.com/mlg/mla-11
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K-means Clustering

K-means is the most widely used clustering algorithm and is typically the first method to try for general clustering tasks.
The scikit-learn KMeans implementation is suitable for small to medium-sized datasets, while Faiss's kmeans is more efficient and accurate for very large datasets.
K-means requires the number of clusters to be specified in advance and relies on the Euclidean distance metric, which performs poorly in high-dimensional spaces.
When document embeddings have high dimensionality (e.g., 768 dimensions from sentence transformers), K-means becomes less effective due to the limitations of Euclidean distance in such spaces.

Alternatives to K-means for High Dimensions

For text embeddings with high dimensionality, agglomerative (hierarchical) clustering methods are preferable, particularly because they allow the use of different similarity metrics.
Agglomerative clustering in scikit-learn accepts a pre-computed cosine similarity matrix, which is more appropriate for natural language processing.
Constructing the pre-computed distance (or similarity) matrix involves normalizing vectors and computing dot products, which can be efficiently achieved with linear algebra libraries like PyTorch.
Hierarchical algorithms do not use inertia in the same way as K-means and instead rely on external metrics, such as silhouette score.
Other clustering algorithms exist, including spectral, mean shift, and affinity propagation, which are not covered in this episode.

Semantic Search and Vector Indexing

Libraries such as Faiss, Annoy, and HNSWlib provide approximate nearest neighbor search for efficient semantic search on large-scale vector data.
These systems create an index of your embeddings to enable rapid similarity search, often with the ability to specify cosine similarity as the metric.
Sample code using these libraries with sentence transformers can be found in the UKP Lab sentence-transformers examples directory.

Determining the Optimal Number of Clusters

Both K-means and agglomerative clustering require a predefined number of clusters, but this is often unknown beforehand.
The "elbow" method involves running the clustering algorithm with varying cluster counts and plotting the inertia (sum of squared distances within clusters) to visually identify the point of diminishing returns; see kmeans.inertia_.
The kneed package can automatically detect the "elbow" or "knee" in the inertia plot, eliminating subjective human judgment; sample code available here.
The silhouette score, calculated via silhouette_score, considers both inter- and intra-cluster distances and allows for direct selection of the number of clusters with the maximum score.
The silhouette score can be computed using a pre-computed distance matrix (such as from cosine similarities), making it well-suited for applications involving non-Euclidean metrics and hierarchical clustering.

Density-Based Clustering: DBSCAN and HDBSCAN

DBSCAN is a hierarchical clustering method that does not require specifying the number of clusters, instead discovering clusters based on data density.
HDBSCAN is a more popular and versatile implementation of density-based clustering, capable of handling various types of data without significant parameter tuning.
DBSCAN and HDBSCAN can be preferable to K-means or agglomerative clustering when automatic determination of cluster count or robustness to noise is important.
However, these algorithms may not perform well with all types of high-dimensional embedding data, as illustrated by the challenges faced when clustering 768-dimensional text embeddings.

Summary Recommendations and Links

For low- to medium-sized, low-dimensional data, use K-means with silhouette score to choose the optimal number of clusters: scikit-learn KMeans, silhouette_score.
For very large data or vector search, use Faiss.kmeans.
For high-dimensional data using cosine similarity, use Agglomerative Clustering with a pre-computed square matrix of cosine similarities; sample code.
For density-based clustering, consider DBSCAN or HDBSCAN.
Exploratory code and further examples can be found in the UKP Lab sentence-transformers examples.

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