SpectralClustering

The SpectralClustering algorithm uses the scikit-learn SpectralClustering clustering algorithm to divide a result set into set of distinct clusters. SpectralClustering first transforms the input data using the Radial Basis Function (rbf) kernel, and then performs K-Means clustering on the result. Consequently, SpectralClustering can learn clusters with a non-convex shape. The cluster for each event is set in a new field named cluster.

Parameters

The k parameter specifies the number of clusters to divide the data into after kernel step. By default, the cluster label field name is cluster. Change that behavior by using the as keyword to specify a different field name.

Syntax

fit SpectralClustering <fields> [k=<int>] [gamma=<float>] [random_state=<int>]

Syntax constraints

You cannot save SpectralClustering models using the into keyword. If you want to be able to predict cluster assignments for future data, you can combine the SpectralClustering algorithm with any clustering algorithm. For example, first cluster the data using SpectralClustering, then fit a classifier to predict the cluster using RandomForestClassifier.

Example

The following example uses SpectralClustering on a test set.

... | fit SpectralClustering * k=3 | stats count by cluster

Local availability Permalink to this section#

• Local class: SpectralClustering
• Source file: Splunk_ML_Toolkit/bin/algos/SpectralClustering.py (in-repo path Splunk_ML_Toolkit/bin/algos/SpectralClustering.py)
• algos.conf stanza: [SpectralClustering]
• Class bases: ClustererMixin, BaseAlgo

Source Permalink to this section#

Adapted from the Splunk AI Toolkit 5.6.4 documentation at /en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/algorithms-and-scoring-metrics-in-the-ai-toolkit/algorithms-in-the-ai-toolkit (section: clusterer).