Interpretable Market Segmentation on High Dimension Data

  1. Eiras-Franco, Carlos
  2. Guijarro-Berdiñas, Bertha
  3. Alonso-Betanzos, Amparo
  4. Bahamonde, Antonio 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Proceedings:
XoveTIC Congress 2018

Year of publication: 2018

Type: Conference paper

DOI: 10.3390/PROCEEDINGS2181171 GOOGLE SCHOLAR lock_openOpen access editor

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.