Interpretable clustering via multi-polytope machines

A computer-implemented method of training a machine to perform unsupervised interpretable machine learning, comprising:

• receiving a dataset having multiple features;
• clustering the dataset into clusters;
• generating hyperplanes in a multi-dimensional feature space of the dataset, the hyperplanes separating pairs of the clusters, wherein a hyperplane separates a pair of clusters;
• repeating the clustering and generating until convergence, wherein the clustering in a subsequent iteration uses the generated hyperplanes from a previous iteration to optimize performance of the clustering;
• adjusting the hyperplanes to further improve the performance of the clustering;
• providing the clusters and interpretation of the clusters, wherein a cluster's interpretation is provided based on hyperplanes that construct a polytope containing the cluster,
• wherein the machine is trained to jointly cluster and interpret resulting clusters of the dataset.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a device to cause the device to:

• receive a dataset having multiple features;
• train to jointly cluster and interpret resulting clusters of the dataset by at least:
• cluster the dataset into clusters;
• generate hyperplanes in a multi-dimensional feature space of the dataset, the hyperplanes separating pairs of the clusters, wherein a hyperplane separates a pair of clusters;
• repeat clustering and generating until convergence, wherein the clustering in a subsequent iteration uses the generated hyperplanes from a previous iteration to optimize performance of the clustering;
• adjust the hyperplanes to further improve the performance of the clustering;
• provide the clusters and interpretation of the clusters, wherein a cluster's interpretation is provided based on hyperplanes that construct a polytope containing the cluster.