Configure DBSCAN for a clustering solution
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- UpdatedFeb 10, 2025
- 3 minutes to read
- Xanadu
- Intelligent Experiences
Consider applying the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to your clustering solution. DBSCAN is available as an alternative to the default clustering algorithm, k-means.
Before you begin
- Create a clustering solution definition or use an existing one.
- Role required: admin or ml_admin
About this task
Predictive Intelligence uses the k-means algorithm by default in its clustering framework. DBSCAN is another clustering algorithm that's also used in data mining and machine learning. Some users prefer DBSCAN as it doesn't require you to specify the number of clusters in the data before clustering. For a summary of the pros and cons for each algorithm, see this conversation and this article.
In this example scenario, you apply DBSCAN to a clustering solution.
Procedure
Related Content
- Create and train a clustering solution
Group similar records into clusters so you can address them collectively or identify patterns.