Density-Based Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm that identifies clusters of arbitrary shapes in noisy spatial data. Use the DBSCAN Prediction component to predict cluster assignments for new data points.
Computing resources
Supported computing resources:
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MaxCompute
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Flink
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DLC
Configure in the PAI console
Configure DBSCAN Prediction parameters in the PAI console.
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Tab |
Parameter |
Description |
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Field Setting |
reservedCols |
Whether to retain original column names. |
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Parameter Setting |
predictionCol |
Prediction column name. |
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predictionDetailCol |
Prediction details column name. |
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numThreads |
Number of threads for DBSCAN clustering. |
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Execution Tuning |
Choose Running Mode |
MaxCompute |
Use MaxCompute or Flink computing resources. For more information about how to configure the number of workers and the memory of workers, see Estimate resource usage. |
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Flink |
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DLC |
Uses DLC computing resources. Configure resources as instructed on the page. |
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Estimate resource usage
Use the following guidelines to size your nodes before running the component.
Memory per node
Allocate approximately 30 times the model size in memory per node.
Example: For a 1 GB model, allocate 30 GB per node.
Number of nodes
Adding nodes improves performance initially, but communication overhead eventually degrades speed. Stop adding nodes when performance decreases.