Interpretable Market Segmentation on High Dimension Data

Proceedings

Alonso-Betanzos, Amparo¹  Guijarro-Berdiñas, Bertha²  Eiras-Franco, Carlos³  Bahamonde, Antonio⁴ 

[1] Author to whom correspondence should be addressed.;Computer Science Department, Universidad de Oviedo, 33203 Gijón, Spain;Grupo LIDIA, CITIC, Universidade da Coruña, 15071 A Coruña, Spain;Presented at the XoveTIC Congress. A Coruña, Spain, 27–28 September 2018.

关键词: market segmentation;    interpretability;    Explainability;    scalability;    Machine Learning;    Big Data;   

DOI  :  10.3390/proceedings2181171

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY