Summary of Detecting Outliers by Clustering Algorithms, By Qi Li et al.
Detecting outliers by clustering algorithms
by Qi Li, Shuliang Wang
First submitted to arxiv on: 7 Dec 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 outlier detection approach called ODAR is proposed for clustering algorithms, enabling them to detect and eliminate outliers without requiring additional processing. Currently, only a few clustering algorithms have this capability, making it tedious to introduce an outlier detection task before each clustering process. The proposed ODAR method maps outliers and normal objects into two separated clusters through feature transformation, allowing any clustering algorithm to detect outliers by identifying clusters. Experimental results show that ODAR is robust across diverse datasets, achieving at least 5% improvement in accuracy on 7 out of 10 tested datasets compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Outliers can ruin the results of clustering algorithms, making it hard for them to group similar objects together. To solve this problem, researchers have developed a new way to detect outliers called ODAR. It works by changing the features of data points so that outliers and normal points are in different groups. This means any clustering algorithm can use ODAR to find outliers and get better results. |
Keywords
» Artificial intelligence » Clustering » Outlier detection
