TAID-LCA: Segmentation Algorithm Based on Ternary Trees

Claudio Castro-López, Purificación Vicente-Galindo, Purificación Galindo-Villardón and Oscar Borrego-Hernández
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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
Oscar Borrego-Hernández: Centro de Estudios de Opinión y Análisis (CEOA), Sarabia 100-A, Universidad Veracruzana, Xalapa 91030, Mexico

Mathematics, 2022, vol. 10, issue 4, 1-16

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.

Keywords: CHAID algorithm; latent class analysis; ? index; CATANOVA; impurity measures; simulate annealing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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