Interpretable market segmentation on high dimension data
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Interpretable market segmentation on high dimension dataData
2018-09-17Cita bibliográfica
Eiras-Franco, C.; Guijarro-Berdiñas, B.; Alonso-Betanzos, A.; Bahamonde, A. Interpretable Market Segmentation on High Dimension Data. Proceedings 2018, 2, 1171.
Resumo
[Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, the interpretability of machine learning algorithms is becoming increasingly relevant and even becoming a legal requirement, all of which increases the demand for such algorithms. In this work we propose a quality measure that factors in the interpretability of results. Additionally, we present a grouping algorithm on dyadic data that returns results with a level of interpretability selected by the user and capable of handling large volumes of data. Experiments show the accuracy of the results, on par with traditional methods, as well as its scalability.
Palabras chave
Market segmentation
Interpretability
Explainability
Scalability
Machine learning
Big Data
Interpretability
Explainability
Scalability
Machine learning
Big Data
Descrición
Trátase dun resumo estendido da ponencia
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
2504-3900