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Can an unsupervised clustering algorithm reproduce a categorization system?

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  • Nathalia Castellanos
  • Dhruv Desai
  • Sebastian Frank
  • Stefano Pasquali
  • Dhagash Mehta

Abstract

Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems' consistency is questioned when they do not align with cohorts from unsupervised clustering algorithms optimized for various metrics. We investigate whether unsupervised clustering can reproduce ground truth classes in a labeled dataset, showing that success depends on feature selection and the chosen distance metric. Using toy datasets and fund categorization as real-world examples we demonstrate that accurately reproducing ground truth classes is challenging. We also highlight the limitations of standard clustering evaluation metrics in identifying the optimal number of clusters relative to the ground truth classes. We then show that if appropriate features are available in the dataset, and a proper distance metric is known (e.g., using a supervised Random Forest-based distance metric learning method), then an unsupervised clustering can indeed reproduce the ground truth classes as distinct clusters.

Suggested Citation

  • Nathalia Castellanos & Dhruv Desai & Sebastian Frank & Stefano Pasquali & Dhagash Mehta, 2024. "Can an unsupervised clustering algorithm reproduce a categorization system?," Papers 2408.10340, arXiv.org.
    • Handle: RePEc:arx:papers:2408.10340
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    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Moreno, David & Marco, Paulina & Olmeda, Ignacio, 2006. "Self-organizing maps could improve the classification of Spanish mutual funds," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1039-1054, October.
    4. Edwin J. Elton & Martin J. Gruber & Christopher R. Blake, 2003. "Incentive Fees and Mutual Funds," Journal of Finance, American Finance Association, vol. 58(2), pages 779-804, April.
    5. Sanjeev Bhojraj & Charles M. C. Lee, 2002. "Who Is My Peer? A Valuation‐Based Approach to the Selection of Comparable Firms," Journal of Accounting Research, Wiley Blackwell, vol. 40(2), pages 407-439, May.
    6. Dhruv Desai & Ashmita Dhiman & Tushar Sharma & Deepika Sharma & Dhagash Mehta & Stefano Pasquali, 2023. "Quantifying Outlierness of Funds from their Categories using Supervised Similarity," Papers 2308.06882, arXiv.org.
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