# Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data

arXiv: https://arxiv.org/abs/2102.11872 · HTML: https://mlsanity.com/paper/2102.11872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [NormFace: L2 Hypersphere Embedding for Face Verification](https://mlsanity.com/paper/1704.06369/) (2017)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [Temporal Phenotyping using Deep Predictive Clustering of Disease Progression](https://mlsanity.com/paper/2006.08600/) (2020)
- [Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU](https://mlsanity.com/paper/1806.02878/) (2018)
- [Simultaneous Clustering and Estimation of Heterogeneous Graphical Models](https://mlsanity.com/paper/1611.09391/) (2016)
- [Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring](https://mlsanity.com/paper/2001.01987/) (2020)
- [Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering](https://mlsanity.com/paper/1605.06711/) (2016)
- [On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning](https://mlsanity.com/paper/1912.00672/) (2019)
- [EC3: Combining Clustering and Classification for Ensemble Learning](https://mlsanity.com/paper/1708.08591/) (2017)
- [Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts](https://mlsanity.com/paper/1605.00959/) (2016)

## Abstract (arXiv)

In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for such combined clustering and classification either 1) are classifier-specific and not generic, or 2) independently perform clustering and classifier training, which may not form clusters that can potentially benefit classifier performance. The question of how to perform clustering to improve the performance of classifiers trained on the clusters has received scant attention in previous literature, despite its importance in several real-world applications. In this paper, first, we theoretically analyze the generalization performance of classifiers trained on clustered data and find conditions under which clustering can potentially aid classification. This motivates the design of a simple k-means-based classification algorithm called Clustering Aware Classification (CAC) and its neural variant {DeepCAC}. DeepCAC effectively leverages deep representation learning to learn latent embeddings and finds clusters in a manner that make the clustered data suitable for training classifiers for each underlying subpopulation. Our experiments on synthetic and real benchmark datasets demonstrate the efficacy of DeepCAC over previous methods for combined clustering and classification.