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TAID-LCA: Segmentation Algorithm Based on Ternary Trees

Author

Listed:
  • Claudio Castro-López

    (Centro de Estudios de Opinión y Análisis (CEOA), Sarabia 100-A, Universidad Veracruzana, Xalapa 91030, Mexico)

  • Purificación Vicente-Galindo

    (Department of Statistics, University of Salamanca, 37008 Salamanca, Spain)

  • Purificación Galindo-Villardón

    (Department of Statistics, University of Salamanca, 37008 Salamanca, Spain
    Centro de Investigación Institucional (CII), Av. Viel 1497, Universidad Bernardo O’Higgins, Santiago 8370993, Chile
    Centro de Gestión de Estudios Estadísticos, Universidad Estatal de Milagro, Milagro 091050, Ecuador)

  • Oscar Borrego-Hernández

    (Centro de Estudios de Opinión y Análisis (CEOA), Sarabia 100-A, Universidad Veracruzana, Xalapa 91030, Mexico)

Abstract

In this work, a statistical method for the segmentation of samples and/or populations is presented, which is based on a ternary tree structure. This approach overcomes known limitations of other segmentation methods such as CHAID, concerning the multivariate response and the non-symmetric relationship between explanatory and response variables. The multivariate response segmentation problem is handled through latent class models, while the factorial decomposition of the explanatory capability of variables is based on the Non-Symmetrical Correspondence Analysis. Stop criteria based on the CATANOVA index and impurity measures are proposed. A Simulated Annealing based post-pruning strategy is considered to avoid over-fitting relative to the training set and guarantee a better generalization capability for the method.

Suggested Citation

  • Claudio Castro-López & Purificación Vicente-Galindo & Purificación Galindo-Villardón & Oscar Borrego-Hernández, 2022. "TAID-LCA: Segmentation Algorithm Based on Ternary Trees," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:560-:d:747242
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    References listed on IDEAS

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