Identify optimal clustering models in seconds.
Extract dataset topology, rank the best unsupervised models, and execute grid-search HPO automatically. A developer-first AutoML platform, accessible via our workbench or REST API.
Iris clustering launch
Select the best clustering algorithm before running a full grid search.
A new species of AutoML tool. Purpose-built for modern data teams and AI pipelines, ClusterMind sets a new standard for unsupervised model selection and hyperparameter optimization.
Automatically profile dataset topology
Upload your raw datasets to automatically extract topological properties like sample density, sparseness, dimensionality, and the Hopkins statistic for clustering tendency.
1.0 Ingest ->
Select the best candidate algorithms
Match dataset topology with the optimal clustering algorithms (K-Means, GMM, DBSCAN, Birch, Spectral, Agglomerative) using our offline meta-learning engine.
2.0 Recommend ->
Automate hyperparameter optimization
Run grid-search HPO instantly to find the best settings (cluster sizes, distance metrics, linkage criteria, covariance types, thresholds) optimized against silhouette, Calinski-Harabasz, and Davies-Bouldin scores.
3.0 Tune ->
Access programmatically with REST API Keys
Integrate clustering directly into your production code, applications, or AI agents. Generate secure API keys to request recommendations and grid-search results on the fly.
4.0 Integrate ->
headers=headers,
files={"file": open("dataset.csv", "rb")}
"recommended_algorithm": "GMM",
"optimal_hyperparams": {
"covariance_type": "full"