Publicado

2019-10-01

A clustering algorithm for ipsative variables

Algoritmo de clusterización para variables ipsativas

DOI:

https://doi.org/10.15446/dyna.v86n211.77835

Palabras clave:

clustering, ipsatives variables, motivational profile (en)
clúster, variables ipsativas, perfil motivacional (es)

Autores/as

The aim of this study is to introduce a new clustering method for ipsatives variables. This  method can be used for nominals or ordinals variables for which responses must be mutually exclusive, and it is independent of data distribution. The proposed method is applied to outline motivational profiles for individuals based on a declared preferences set.  A case study is used to analyze the performance of the proposed algorithm by comparing proposed method results versus the PAM method. Results show that proposed method generate a better segmentation and differentiated groups. An extensive study was conducted to validate the performance clustering method against a set of random groups by clustering measures.

El objetivo del estudio es presentar un nuevo método de agrupamiento para variables ipsativas. Este método se puede usar para variables nominales u ordinales para las cuales las respuestas deben ser mutuamente excluyentes, y es independiente de la distribución de datos. El método propuesto se aplica para delinear los perfiles motivacionales para los individuos con base en un conjunto de preferencias declaradas. Se utiliza un estudio de caso para analizar el rendimiento del algoritmo propuesto comparando los resultados del método propuesto con el método PAM. Los resultados muestran que el método propuesto genera una mejor segmentación y grupos diferenciados. Se llevó a cabo una extensión del estudio para validar el desempeño del método propuesto contra un conjunto de clústeres aleatorios mediante medidas de agrupamiento.

Referencias

Calderón-Carvajal, C. y Ximénez-Gómez, C. Análisis factorial de ítems de respuesta forzada: una revisión y un ejemplo, Revista Latinoamericana de Psicología, pp. 24-34. DOI: 10.1016/S0120-0534(14)70003-2, 2014.

Toro-Dupouy, L., Arias-Aranda, D., Rodríguez-Duarte, A. and Bou-Bouzá, G., Profiles of human resources managers according to their perceptions about the impact of diversity on firm performance, Dyna Ingeniería e Industria Spain, 92(6), pp. 616, 2017. DOI: 10.6036/8581 [3] Liu, J., Liao, X., Huang, W. and Yang, J., A new decision-making approach for multiple criteria sorting with an imbalanced set of assignment examples, European Journal of Operational Research, 265(2), pp. 598-620, 2018. DOI: 10.1016/j.ejor.2017.07.043.

Xu, R. and Wunsch II, D., Clustering, IEEE Computational Intelligence Magazine, 4(3), pp. 92-95, 2009. DOI: 10.1109/mci.2009.933101

Jacques, J. and Biernacki, C., Model-based clustering for multivariate partial ranking data, Journal of Statistical Planning and Inference, 149, pp. 201-217, 2014. HAL Id: hal-00743384

Barthélemyr, J.P., Brucker, F. and Osswald, C., Combinatorial optimisation and hierarchical classifications, Annals of Operations Research, 153(1), pp. 179-214, 2007. DOI: 10.1007/s10288-004-0051-9

Giordan, M. and Diana, G., A clustering method for categorical ordinal data, Communications in Statistics—Theory and Methods, 40(7), pp. 1315-1334, 2011. DOI: 10.1080/03610920903581010

Şeref, O., Fan, Y.J., Borenstein, E. and Chaovalitwongse, W.A., Information-theoretic feature selection with discrete (k) - median clustering, Annals of Operations Research, 263(1-2), pp. 93-118, 2018. DOI: 10.1007/s10479-014-1589-3

Silvestre, C., Cardoso, M.G. and Figueiredo, M.A., Identifying the number of clusters in discrete mixture models, arXiv preprint, 2014. arXiv: 1409.7419

Biernacki, C. and Jacques, J., Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm, Statistics and Computing, 26(5), pp. 929-943, 2016. hal-01052447v2

Vermunt, J. and Magidson, J., Technical guide for latent GOLD 4.0: basic and advanced, Statistical Innovations Inc., Belmont, 2005.

Murphy, T. and Martin, D., Mixtures of distance - based models for ranking data, Computational Statistics and Data Analysis, 41(3-4), pp. 645-655, 2003. DOI: 10.1016/S0167-9473(02)00165-2

Diaconis, P., A generalization of spectral analysis with application to ranked data, The Annals of Statistics, 17(3), pp. 949-979, 1989. DOI: 10.1214/aos/1176347251

Busse, L.M., Orbanz, P. and Buhmann, J.M., Cluster analysis of heterogeneous rank data, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 113-120.

Lee, P. and Yu, P., Mixtures of weighted distance-based models for ranking data with applications in political studies, Computational Statistics and Data Analysis, 56(8), pp. 2486-2500, 2012. DOI: 10.1016/j.csda.2012.02.002

Benter, W., Computer based horse race handicapping and wagering systems: a report. Efficiency of Racetrack Betting Markets, pp. 183-198, 2008. DOI: 10.1142/9789812819192_0019

Gormley, I.C. and Murphy, T.B., Analysis of Irish third? level college applications data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169(2), pp. 361-379, 2006. DOI: 10.1111/j.1467-985X.2006.00412.x

Gormley, I.C. and Murphy, T.B., Exploring voting blocs within the Irish electorate: a mixture modeling approach. Journal of the American Statistical Association, 103(483), 2008. pp. 1014-1027. DOI: 10.1198/016214507000001049

Gormley, I.C. and Murphy, T.B., A mixture of experts model for rank data with applications in election studies. The Annals of Applied Statistics, 2(4), pp. 1452-1477, 2008. DOI: 10.1214/08-AOAS178

Thurstone, L.L., A law of comparative judgment. Psychological Review, 34(4), pp. 273-286, 1927. DOI: 10.1037/h0070288.

Borgwardt, S., Brieden, A. and Gritzmann, P., An LP-based k-means algorithm for balancing weighted point sets. European Journal of Operational Research, 263(2), pp. 349-355, 2017. DOI: 10.1016/j.ejor.2017.04.054

Bock, H., Clustering methods: a history of k-Means algorithms, in: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds), Selected contributions in data analysis and classification. Studies in classification, data analysis, and knowledge organization. Springer, Berlin, Heidelberg, 2007, pp. 161-172. DOI: 10.1007/978-3-540-73560-1_15

Kaufman, L. and Rousseeuw, P.J., Finding groups in data: an introduction to cluster analysis, Wiley, New Jersey, 2008.

Sorensen, T.A., A method of establishing groups of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of vegetation in Danish commons, K dan Vidensk Selsk Biol. Skr. 5, pp. 1-40, 1948.

Santi, É., Aloise, D. and Blanchard, S.J., A model for clustering data from heterogeneous dissimilarities, European Journal of Operational Research, 253(3), pp. 659-672, 2016. DOI: 10.1016/j.ejor.2016.03.033

Rousseeuw, P., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, pp. 53-65, 1987. DOI: 10.1016/0377-0427(87)90125-7

Beaman, J. and Vaske, J.J., An ipsative clustering model for analyzing attitudinal data. Journal of Leisure Research, 27(2), pp. 168-191, 1995. DOI: 10.1080/00222216.1995.11949741

Toro, F. y Cabrera, H., Distinciones y relaciones entre clima, motivación, satisfacción y cultura organizacional, Revista Interamericana de Psicología Organizacional, 17(2), pp. 27-39, 1998.

Zaki, M.J. and Meira, W., Data mining and analysis: fundamental concepts and algorithms, Cambridge University Press, New York, USA, 2014.

Cómo citar

IEEE

[1]
J. Rubiano Moreno, C. Alonso Malaver, S. Nucamendi Guillén, y C. López Hernández, «A clustering algorithm for ipsative variables», DYNA, vol. 86, n.º 211, pp. 94–101, oct. 2019.

ACM

[1]
Rubiano Moreno, J., Alonso Malaver, C., Nucamendi Guillén, S. y López Hernández, C. 2019. A clustering algorithm for ipsative variables. DYNA. 86, 211 (oct. 2019), 94–101. DOI:https://doi.org/10.15446/dyna.v86n211.77835.

ACS

(1)
Rubiano Moreno, J.; Alonso Malaver, C.; Nucamendi Guillén, S.; López Hernández, C. A clustering algorithm for ipsative variables. DYNA 2019, 86, 94-101.

APA

Rubiano Moreno, J., Alonso Malaver, C., Nucamendi Guillén, S. & López Hernández, C. (2019). A clustering algorithm for ipsative variables. DYNA, 86(211), 94–101. https://doi.org/10.15446/dyna.v86n211.77835

ABNT

RUBIANO MORENO, J.; ALONSO MALAVER, C.; NUCAMENDI GUILLÉN, S.; LÓPEZ HERNÁNDEZ, C. A clustering algorithm for ipsative variables. DYNA, [S. l.], v. 86, n. 211, p. 94–101, 2019. DOI: 10.15446/dyna.v86n211.77835. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/77835. Acesso em: 18 mar. 2026.

Chicago

Rubiano Moreno, Jesica, Carlos Alonso Malaver, Samuel Nucamendi Guillén, y Carlos López Hernández. 2019. «A clustering algorithm for ipsative variables». DYNA 86 (211):94-101. https://doi.org/10.15446/dyna.v86n211.77835.

Harvard

Rubiano Moreno, J., Alonso Malaver, C., Nucamendi Guillén, S. y López Hernández, C. (2019) «A clustering algorithm for ipsative variables», DYNA, 86(211), pp. 94–101. doi: 10.15446/dyna.v86n211.77835.

MLA

Rubiano Moreno, J., C. Alonso Malaver, S. Nucamendi Guillén, y C. López Hernández. «A clustering algorithm for ipsative variables». DYNA, vol. 86, n.º 211, octubre de 2019, pp. 94-101, doi:10.15446/dyna.v86n211.77835.

Turabian

Rubiano Moreno, Jesica, Carlos Alonso Malaver, Samuel Nucamendi Guillén, y Carlos López Hernández. «A clustering algorithm for ipsative variables». DYNA 86, no. 211 (octubre 1, 2019): 94–101. Accedido marzo 18, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/77835.

Vancouver

1.
Rubiano Moreno J, Alonso Malaver C, Nucamendi Guillén S, López Hernández C. A clustering algorithm for ipsative variables. DYNA [Internet]. 1 de octubre de 2019 [citado 18 de marzo de 2026];86(211):94-101. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/77835

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CrossRef Cited-by

CrossRef citations1

1. Jessica Rubiano-Moreno, Carlos Alonso-Malaver, Samuel Nucamendi-Guillén, Carlos López-Hernández, Camilo Ramírez-Rojas. (2023). Work Motivation Profiles of the Millennial Generation. Revista CEA, 9(21), p.e2603. https://doi.org/10.22430/24223182.2603.

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