Interpretable clustering via multi-polytope machines

Patent metadata

Field	Value
Publication number	US-2023267339-A1
Application number	US-202217675202-A
Country	US
Kind code	A1
Filing date	Feb 18, 2022
Priority date	Feb 18, 2022
Publication date	Aug 24, 2023
Grant date	—

Abstract

In unsupervised interpretable machine learning, one or more datasets having multiple features can be received. A machine can be trained to jointly cluster and interpret resulting clusters of the dataset by at least jointly clustering the dataset into clusters and generating hyperplanes in a multi-dimensional feature space of the dataset, where the hyperplanes separate pairs of the clusters, where a hyperplane separates a pair of clusters. Jointly clustering the dataset into clusters and generating hyperplanes can repeat until convergence, where the clustering in a subsequent iteration uses the generated hyperplanes from a previous iteration to optimize performance of the clustering. The hyperplanes can be adjusted to further improve the performance of the clustering. The clusters and interpretation of the clusters can be provided, where a cluster's interpretation is provided based on hyperplanes that construct a polytope containing the cluster.

First claim

What is claimed is:

1 . A system for training a machine to perform unsupervised interpretable machine learning, comprising:
at least one processor; and a memory device coupled with the at least one processor; the at least one processor configured to at least:
receive a dataset having multiple features;
train to jointly cluster and interpret resulting clusters of the dataset by at least:
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.

2 . The system of claim 1 , wherein the clustering and the generating of the hyperplanes are performed as a single mixed integer non-linear programming that solves alternating minimization between the clustering and the hyperplane generating.

3 . The system of claim 1 , wherein the clustering is implemented using a representation aware k-means clustering that clusters with awareness of representation error using a clustering metric.

4 . The system of claim 1 , wherein the hyperplanes are generated based on configurable parameters that control sparsity of the hyperplanes for interpretability.

5 . The system of claim 1 , wherein the adjusting of the hyperplanes is performed based on a selected clustering metric.

6 . The system of claim 5 , wherein the selected clustering metric includes Silhouette index.

7 . The system of claim 5 , wherein the selected clustering metric includes Dunn index.

8 . 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.

9 . The computer-implemented method of claim 8 , wherein the clustering and the generating of the hyperplanes are performed as a single mixed integer non-linear programming that solves alternating minimization between the clustering and the hyperplane generating.

10 . The computer-implemented method of claim 8 , wherein the clustering is implemented using a representation aware k-means clustering that clusters with awareness of representation error using a clustering metric.

11 . The computer-implemented method of claim 8 , wherein the hyperplanes are generated based on configurable parameters that control sparsity of the hyperplanes for interpretability.

12 . The computer-implemented method of claim 8 , wherein the adjusting of the hyperplanes is performed based on a selected clustering metric.

13 . The computer-implemented method of claim 12 , wherein the selected clustering metric includes Silhouette index.

14 . The computer-implemented method of claim 12 , wherein the selected clustering metric includes Dunn index.

15 . 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.

16 . The computer program product of claim 15 , wherein the clustering and the generating of the hyperplanes are performed as a single mixed integer non-linear programming that solves alternating minimization between the clustering and the hyperplane generating.

17 . The computer program product of claim 15 , wherein the clustering is implemented using a representation aware k-means clustering that clusters with awareness of representation error using a clustering metric.

18 . The computer program product of claim 15 , wherein the hyperplanes are generated based on configurable parameters that control sparsity of the hyperplanes for interpretability.

19 . The computer program product of claim 15 , wherein the adjusting of the hyperplanes is performed based on a selected clustering metric.

20 . The computer program product of claim 19 , wherein the selected clustering metric includes at least one selected from the group of Silhouette index and Dunn index.

Assignees

• IBM

Inventors

• Phan Dzung Tien
• Lawless Connor Aram
• Kalagnanam Jayant R
• Nguyen Lam Minh
• Reddy Chandrasekhara K

Classifications

• G06N20/10 Primary

  using kernel methods, e.g. support vector machines [SVM] · CPC title

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title