Summary of A Simulation Study Of Cluster Search Algorithms in Data Set Generated by Gaussian Mixture Models, By Ryosuke Motegi and Yoichi Seki
A simulation study of cluster search algorithms in data set generated by Gaussian mixture models
by Ryosuke Motegi, Yoichi Seki
First submitted to arxiv on: 27 Jul 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 In this study, researchers compare and evaluate various algorithms for determining the number of clusters in data clustering. The algorithms tested include centroid-based methods using Euclidean distance and model-based methods using Gaussian mixture models (GMMs). The results show that some criteria based on Euclidean distance can lead to unreasonable decisions when cluster overlap is present. Additionally, the study finds that model-based algorithms are more robust to covariance type and cluster overlap compared to centroid-based methods, as long as the sample size is sufficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at different ways to find the number of groups in a set of data. It compares two kinds of methods: ones that use a central point (called a centroid) and ones that use statistical models. The results show that when the clusters are mixed together, some methods can make bad decisions. Also, it found that one kind of method is better than another at dealing with certain kinds of differences in how data points are related. |
Keywords
» Artificial intelligence » Clustering » Euclidean distance
