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GINDCLUS: Generalized INDCLUS with External Information

Author

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  • Laura Bocci

    (Sapienza University of Rome)

  • Donatella Vicari

    (Sapienza University of Rome)

Abstract

A Generalized INDCLUS model, termed GINDCLUS, is presented for clustering three-way two-mode proximity data. In order to account for the heterogeneity of the data, both a partition of the subjects into homogeneous classes and a covering of the objects into groups are simultaneously determined. Furthermore, the availability of information which is external to the three-way data is exploited to better account for such heterogeneity: the weights of both classifications are linearly linked to external variables allowing for the identification of meaningful classes of subjects and groups of objects. The model is fitted in a least-squares framework, and an efficient Alternating Least-Squares algorithm is provided. An extensive simulation study and an application on benchmark data are also presented.

Suggested Citation

  • Laura Bocci & Donatella Vicari, 2017. "GINDCLUS: Generalized INDCLUS with External Information," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 355-381, June.
    • Handle: RePEc:spr:psycho:v:82:y:2017:i:2:d:10.1007_s11336-016-9526-9
    DOI: 10.1007/s11336-016-9526-9
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    References listed on IDEAS

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    1. Donatella Vicari & Maurizio Vichi, 2009. "Structural Classification Analysis of Three-Way Dissimilarity Data," Journal of Classification, Springer;The Classification Society, vol. 26(2), pages 121-154, August.
    2. J. Carroll & Phipps Arabie, 1983. "Indclus: An individual differences generalization of the adclus model and the mapclus algorithm," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 157-169, June.
    3. Jos M.F. Berge & Henk A.L. Kiers, 2005. "A Comparison of Two Methods for Fitting the INDCLUS Model," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 273-286, September.
    4. Paolo Giordani & Henk Kiers, 2012. "FINDCLUS: Fuzzy INdividual Differences CLUStering," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 170-198, July.
    5. Henk Kiers & Donatella Vicari & Maurizio Vichi, 2005. "Simultaneous classification and multidimensional scaling with external information," Psychometrika, Springer;The Psychometric Society, vol. 70(3), pages 433-460, September.
    6. Tom Wilderjans & Dirk Depril & Iven Mechelen, 2012. "Block-Relaxation Approaches for Fitting the INDCLUS Model," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 277-296, October.
    7. Michel Wedel & Wayne DeSarbo, 1998. "Mixtures of (constrained) ultrametric trees," Psychometrika, Springer;The Psychometric Society, vol. 63(4), pages 419-443, December.
    8. Suzanne Winsberg & Geert Soete, 1993. "A latent class approach to fitting the weighted Euclidean model, clascal," Psychometrika, Springer;The Psychometric Society, vol. 58(2), pages 315-330, June.
    9. Bocci, Laura & Vicari, Donatella & Vichi, Maurizio, 2006. "A mixture model for the classification of three-way proximity data," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1625-1654, April.
    10. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    11. Laura Bocci & Maurizio Vichi, 2011. "The K-INDSCAL Model for Heterogeneous Three-Way Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 76(4), pages 691-714, October.
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    Cited by:

    1. Laura Bocci & Donatella Vicari, 2019. "ROOTCLUS: Searching for “ROOT CLUSters” in Three-Way Proximity Data," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 941-985, December.

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