Use clustering algorithms to assign each data point to a group (cluster) based on the similarities with other data points. For some algorithms, you need to specify the number of clusters to be found as a parameter. Other algorithms detect the number of clusters themselves.
The following algorithms are available:
- DCPY.AFFINITYPROPCLUST(max_iter, convergence_iter, damping, preference, columns)
- DCPY.AGGLOCLUST(n_clusters, affinity, linkage, columns)
- DCPY.AUTOCLUST(max_clusters, columns)
- DCPY.BIRCHCLUST(n_clusters, threshold, branching_factor, columns)
- DCPY.DBSCANCLUST(min_samples, eps, columns)
- DCPY.KMEANSCLUST(n_clusters, random_state, init, n_init, max_iter, columns)
- DCPY.MEANSHIFTCLUST(cluster_all, bandwidth, columns)
- DCPY.MINIBATCHKMEANSCLUST(n_clusters, random_state, init, n_init, batch_size, max_no_improvement, max_iter, columns)
- DCPY.SPECTRALCLUST(n_clusters, random_state, n_init, columns)
- MLLIB.BICLUSTER(imputer, n_clusters, seed, columns)
- MLLIB.CLUSTER(imputer, n_clusters, n_iter, columns)
- MLLIB.GMMCLUSTER(imputer, n_clusters, columns)
The SCRIPT function can be used to run your custom algorithm based on the available AI connection. For details, see Add script calculations.