Summary of Clustering Survival Data Using a Mixture Of Non-parametric Experts, by Gabriel Buginga et al.
Clustering Survival Data using a Mixture of Non-parametric Experts
by Gabriel Buginga, Edmundo de Souza e Silva
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel algorithm for survival analysis called SurvMixClust integrates clustering with survival function prediction to predict the timing of future events across various fields. The method learns latent representations for clustering while predicting individual survival functions using a mixture of non-parametric experts. On five public datasets, SurvMixClust outperforms clustering baselines and competes with non-clustering survival models in predictive accuracy, as measured by the time-dependent c-index and log-rank metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Survival analysis helps predict when future events will happen. Imagine trying to figure out how long someone might live or when a customer might switch to a different company. This study creates a new way of doing this called SurvMixClust, which combines two things: grouping similar people together and predicting individual outcomes. It’s like grouping students by their math skills and then predicting how well each student will do on the test. The results show that SurvMixClust does a great job at both clustering and predicting survival curves. |
Keywords
» Artificial intelligence » Clustering
