# Fitting Multiple Machine Learning Models with Performance Based Clustering

arXiv: https://arxiv.org/abs/2411.06572 · HTML: https://mlsanity.com/paper/2411.06572/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Structural Deep Clustering Network](https://mlsanity.com/paper/2002.01633/) (2020)
- [Learning Representation for Clustering via Prototype Scattering and Positive Sampling](https://mlsanity.com/paper/2111.11821/) (2021)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis](https://mlsanity.com/paper/2003.01351/) (2020)
- [Subspace Clustering via Optimal Direction Search](https://mlsanity.com/paper/1706.03860/) (2017)

## Built on by (most cited first, 1 of 1)

- ["A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations](https://mlsanity.com/paper/2509.09073/) (2025)

## Abstract (arXiv)

Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data according to the relations between the features and the target values and we obtain multiple separate models to learn different parts of the data. We further extend our framework to applications having streaming data where we produce outcomes using an ensemble of models. For this, the ensemble weights are updated based on the incoming data batches. We demonstrate the performance of our approach over the widely-studied real life datasets, showing significant improvements over the traditional single-model approaches.