# The Utility of Clustering in Prediction Tasks

arXiv: https://arxiv.org/abs/1509.06163 · HTML: https://mlsanity.com/paper/1509.06163/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2015.

## Built on by (most cited first, 5 of 5)

- [Sequential Disentanglement by Extracting Static Information From A Single Sequence Element](https://mlsanity.com/paper/2406.18131/) (2024)
- [Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records](https://mlsanity.com/paper/2308.10781/) (2023)
- [Cross-Cluster Weighted Forests](https://mlsanity.com/paper/2105.07610/) (2021)
- [A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago](https://mlsanity.com/paper/2109.03433/) (2021)
- [Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models](https://mlsanity.com/paper/2205.03316/) (2022)

## Abstract (arXiv)

We explore the utility of clustering in reducing error in various prediction tasks. Previous work has hinted at the improvement in prediction accuracy attributed to clustering algorithms if used to pre-process the data. In this work we more deeply investigate the direct utility of using clustering to improve prediction accuracy and provide explanations for why this may be so. We look at a number of datasets, run k-means at different scales and for each scale we train predictors. This produces k sets of predictions. These predictions are then combined by a na"ive ensemble. We observed that this use of a predictor in conjunction with clustering improved the prediction accuracy in most datasets. We believe this indicates the predictive utility of exploiting structure in the data and the data compression handed over by clustering. We also found that using this method improves upon the prediction of even a Random Forests predictor which suggests this method is providing a novel, and useful source of variance in the prediction process.