IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i4p560-d747242.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/4/560/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/4/560/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dejan Djordjevic & Dragan Cockalo & Srdjan Bogetic & Mihalj Bakator, 2021. "Predicting Entrepreneurial Intentions among the Youth in Serbia with a Classification Decision Tree Model with the QUEST Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-27, June.
    2. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    3. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    4. Tânia Gonçalves & Lina Lourenço-Gomes & Lígia Pinto, 2020. "Modelling consumer preferences heterogeneity in emerging wine markets: a latent class analysis," Applied Economics, Taylor & Francis Journals, vol. 52(56), pages 6136-6144, December.
    5. E.H.L. Aarts & P.J.M. van Laarhoven, 1989. "Simulated annealing: An introduction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 43(1), pages 31-52, March.
    6. Daniel Oberski & Geert Kollenburg & Jeroen Vermunt, 2013. "A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 267-279, September.
    7. Nanny Wermuth & D.R. Cox, 1998. "On the Application of Conditional Independence to Ordinal Data," International Statistical Review, International Statistical Institute, vol. 66(2), pages 181-199, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    2. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    3. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    4. Ghosh, Atish R. & Qureshi, Mahvash S. & Kim, Jun Il & Zalduendo, Juan, 2014. "Surges," Journal of International Economics, Elsevier, vol. 92(2), pages 266-285.
      • Mahvash S Qureshi & Mr. Atish R. Ghosh & Mr. Juan Zalduendo & Mr. Jun I Kim, 2012. "Surges," IMF Working Papers 2012/022, International Monetary Fund.
    5. Tomàs Aluja-Banet & Eduard Nafria, 2003. "Stability and scalability in decision trees," Computational Statistics, Springer, vol. 18(3), pages 505-520, September.
    6. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    7. Schwartz, Ira M. & York, Peter & Nowakowski-Sims, Eva & Ramos-Hernandez, Ana, 2017. "Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience," Children and Youth Services Review, Elsevier, vol. 81(C), pages 309-320.
    8. Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
    9. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    10. Lisa Blaydes, 2023. "Assessing the Labor Conditions of Migrant Domestic Workers in the Arab Gulf States," ILR Review, Cornell University, ILR School, vol. 76(4), pages 724-747, August.
    11. Ralf Elsner & Manfred Krafft & Arnd Huchzermeier, 2003. "Optimizing Rhenania's Mail-Order Business Through Dynamic Multilevel Modeling (DMLM)," Interfaces, INFORMS, vol. 33(1), pages 50-66, February.
    12. Jindřich Špička & Zdeňka Náglová, 2022. "Consumer segmentation in the meat market - The case study of Czech Republic," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(2), pages 68-77.
    13. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    14. Nicholas T. Davis & Kirby Goidel & Yikai Zhao, 2021. "The Meanings of Democracy among Mass Publics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(3), pages 849-921, February.
    15. Osman Taylan & Abdulaziz S. Alkabaa & Mustafa Tahsin Yılmaz, 2022. "Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    16. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    17. Carter, Virginia & Derudder, Ben & Henríquez, Cristián, 2021. "Assessing local governments’ perception of the potential implementation of biophilic urbanism in Chile: A latent class approach," Land Use Policy, Elsevier, vol. 101(C).
    18. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    19. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    20. Assem Abu Hatab & Padmaja Ravula & Swamikannu Nedumaran & Carl-Johan Lagerkvist, 2022. "Perceptions of the impacts of urban sprawl among urban and peri-urban dwellers of Hyderabad, India: a Latent class clustering analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12787-12812, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:560-:d:747242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.