Clustering

How it is used

Common algorithms are k-means, DBSCAN and hierarchical clustering. Each group is a cluster, and items are said to 'cluster together'. It is often run on embeddings to find themes in large collections of texts or images.

Example sentences

1. Clustering the complaints revealed three main themes: delivery, refunds and app crashes.

2. We ran clustering on the embeddings and then named each cluster by hand.

Common collocations

• k-means clustering
• run clustering
• cluster analysis
• customer clustering

Related terms

• unsupervised learningA type of machine learning that finds structure in data without correct answers (labels), for example grouping similar customers or spotting unusual events.
• embeddingA list of numbers (a vector) that represents a word, sentence, image or other item in such a way that items with similar meanings end up close to each other.
• featureOne measurable piece of information given to a model as input, for example a customer's age, the number of past orders or the brightness of a pixel.
• datasetA collection of examples, such as images, texts or table rows, gathered and organized for training, testing or analyzing a model.

This word in other fields

The same English word can have a different meaning in another field:

• clustering · Software Engineering
• clustering · Data Science & Analytics

More terms: Machine Learning Basics

• artificial intelligence
• machine learning
• model
• supervised learning
• classification
• regression
• reinforcement learning
• state-of-the-art