Clustering and unsupervised labelling of S&P 500 companies using self-organising maps

Baldoni, Chiara (A.A. 2024/2025) Clustering and unsupervised labelling of S&P 500 companies using self-organising maps. Tesi di Laurea in Data analysis for business, Luiss Guido Carli, relatore Nina Deliu, pp. 53. [Bachelor's Degree Thesis]

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Abstract/Index

Methodology. K-means clustering. Self-organizing maps (SOMs). Enhancing SOMs: unsupervised labelling. An unsupervised data clustering and labelling method. Application. Dataset. ⁠SOM training. SOM node clustering via k-means. Extraction of salient dimensions. Label assignment. Discussion.

References

Bibliografia: pp. 52-53.

Thesis Type: Bachelor's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18)
Chair: Data analysis for business
Thesis Supervisor: Deliu, Nina
Academic Year: 2024/2025
Session: Summer
Deposited by: Alessandro Perfetti
Date Deposited: 03 Dec 2025 16:21
Last Modified: 03 Dec 2025 16:21
URI: https://tesi.luiss.it/id/eprint/44202

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