Summary of Can An Unsupervised Clustering Algorithm Reproduce a Categorization System?, by Nathalia Castellanos et al.
Can an unsupervised clustering algorithm reproduce a categorization system?
by Nathalia Castellanos, Dhruv Desai, Sebastian Frank, Stefano Pasquali, Dhagash Mehta
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistical Finance (q-fin.ST); Applications (stat.AP)
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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 approach to peer analysis in investment management uses unsupervised clustering algorithms to categorize assets, challenging traditional expert-provided systems. The study investigates whether these algorithms can accurately reproduce ground truth classes using labeled datasets and demonstrates that success depends on feature selection and distance metrics. Using toy datasets and real-world examples of fund categorization, the authors show that reproducing ground truth classes is difficult without careful selection of features and a suitable distance metric. Furthermore, they highlight limitations in standard clustering evaluation metrics for identifying optimal cluster numbers relative to ground truth classes. By employing supervised Random Forest-based distance metric learning methods, the study demonstrates that unsupervised clustering can effectively reproduce ground truth classes as distinct clusters when appropriate features are available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unsupervised clustering algorithms can help investment management by categorizing assets more accurately. This study tests these algorithms using labeled datasets and shows that they can be successful if the right features are chosen and a good distance metric is used. The authors use simple examples to demonstrate the challenges of reproducing ground truth classes and highlight limitations in common evaluation metrics. By learning from labeled data, unsupervised clustering can even reproduce ground truth classes as distinct groups. |
Keywords
* Artificial intelligence * Clustering * Feature selection * Random forest * Supervised * Unsupervised
