Summary of Towards a Unified Framework Of Clustering-based Anomaly Detection, by Zeyu Fang et al.
Towards a Unified Framework of Clustering-based Anomaly Detection
by Zeyu Fang, Ming Gu, Sheng Zhou, Jiawei Chen, Qiaoyu Tan, Haishuai Wang, Jiajun Bu
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a novel probabilistic mixture model for Unsupervised Anomaly Detection (UAD) that leverages representation learning and clustering to identify abnormal patterns in data. The model establishes a theoretical connection among these components, allowing them to collaboratively benefit anomaly detection performance. By maximizing an anomaly-aware data likelihood, the approach reduces the impact of anomalous data and derives a theoretically substantiated anomaly score. This is further improved by drawing inspiration from gravitational analysis in physics. Experimental results on 30 diverse datasets, involving 17 baseline methods, demonstrate the effectiveness and generalization capability of the proposed method, outperforming state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unsupervised Anomaly Detection helps find unusual patterns in data without labeled examples. This is important for many fields like healthcare or finance. The paper proposes a new way to combine three techniques: representation learning, clustering, and anomaly detection. By using these together, the model can better identify abnormal patterns and reduce the impact of noisy data. The authors also come up with a new way to measure how well their method does. They test it on 30 different datasets and show that it performs better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Clustering » Generalization » Likelihood » Mixture model » Representation learning » Unsupervised
