Summary of K-means Clustering with Incomplete Data with the Use Of Mahalanobis Distances, by Lovis Kwasi Armah et al.
K-Means Clustering With Incomplete Data with the Use of Mahalanobis Distances
by Lovis Kwasi Armah, Igor Melnykov
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 investigates ways to improve the effectiveness of the K-means algorithm when dealing with data containing missing values. The authors highlight the importance of this problem, as many applications rely on K-means clustering. Recent studies have shown that integrating imputation directly into the K-means algorithm leads to better results compared to handling imputation separately. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a popular tool for grouping similar things together (K-means) work better when some of the data is missing. Right now, people usually fix the missing values first and then use K-means. But this paper shows that it’s actually better to do both steps at the same time. |
Keywords
» Artificial intelligence » Clustering » K means
