Summary of A Unified Framework For Center-based Clustering Of Distributed Data, by Aleksandar Armacki et al.
A Unified Framework for Center-based Clustering of Distributed Data
by Aleksandar Armacki, Dragana Bajović, Dušan Jakovetić, Soummya Kar
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
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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 The proposed Distributed Gradient Clustering (DGC-) family of algorithms enables users to find a clustering of joint data while only communicating with their immediate neighbors. The algorithms are parametrized by , controlling the proximity of center estimates, and determining the clustering loss. DGC-accommodates various smooth convex loss functions, including K-means and Huber loss, yielding novel distributed algorithms like DGC-KM_and DGC-HL_. The authors provide a unified analysis, establishing convergence to fixed points under mild assumptions. They also demonstrate the efficacy of their methods on synthetic and real data, highlighting potential applications in outlier detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way for people connected over the internet to group similar things together. This method lets each person only talk to those nearby, but still finds an overall group pattern. The approach uses different formulas to decide what makes something similar or not, like K-means and Huber loss. They showed that their method works well and can even be used for tasks like finding unusual items in a dataset. |
Keywords
* Artificial intelligence * Clustering * K means * Outlier detection
