Summary of Imbalanced Data Clustering Using Equilibrium K-means, by Yudong He
Imbalanced Data Clustering using Equilibrium K-Means
by Yudong He
First submitted to arxiv on: 22 Feb 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 This paper proposes a new clustering algorithm called equilibrium K-means (EKM) to address the issue of learning bias towards large clusters in centroid-based clustering algorithms. The traditional approaches, such as hard K-means (HKM) and fuzzy K-means (FKM), tend to have their centroids crowded in large clusters, which can compromise performance when dealing with imbalanced data. EKM introduces a novel centroid repulsion mechanism based on the Boltzmann operator, where larger clusters repel more, effectively mitigating the learning bias issue. The algorithm is simple, resource-saving, and scalable to large datasets via batch learning. Experimental results show that EKM performs competitively on balanced data and significantly outperforms benchmark algorithms on imbalanced data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way of grouping similar things together called equilibrium K-means (EKM). The old ways of doing this, like hard K-means and fuzzy K-means, have problems when the groups are different sizes. EKM helps by making the centroids move away from each other more when they’re in bigger groups. This makes it better at finding the right groups even when some groups are much smaller than others. |
Keywords
* Artificial intelligence * Clustering * K means
