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ROOTCLUS: Searching for “ROOT CLUSters” in Three-Way Proximity Data

Author

Listed:
  • Laura Bocci

    (Sapienza University of Rome)

  • Donatella Vicari

    (Sapienza University of Rome)

Abstract

In the context of three-way proximity data, an INDCLUS-type model is presented to address the issue of subject heterogeneity regarding the perception of object pairwise similarity. A model, termed ROOTCLUS, is presented that allows for the detection of a subset of objects whose similarities are described in terms of non-overlapping clusters (ROOT CLUSters) common across all subjects. For the other objects, Individual partitions, which are subject specific, are allowed where clusters are linked one-to-one to the Root clusters. A sound ALS-type algorithm to fit the model to data is presented. The novel method is evaluated in an extensive simulation study and illustrated with empirical data sets.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:psycho:v:84:y:2019:i:4:d:10.1007_s11336-019-09686-1
    DOI: 10.1007/s11336-019-09686-1
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    References listed on IDEAS

    as
    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. Rocci, Roberto & Vichi, Maurizio, 2008. "Two-mode multi-partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1984-2003, January.
    3. Anil Chaturvedi & J. Douglas Carroll, 2006. "CLUSCALE ("CLUstering and multidimensional SCAL[E]ing"): A Three-Way Hybrid Model Incorporating Overlapping Clustering and Multidimensional Scaling Structure," Journal of Classification, Springer;The Classification Society, vol. 23(2), pages 269-299, September.
    4. J. Carroll & Jih-Jie Chang, 1970. "Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 283-319, September.
    5. 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.
    6. Laura Bocci & Donatella Vicari, 2017. "GINDCLUS: Generalized INDCLUS with External Information," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 355-381, June.
    7. Henk Kiers, 1997. "A modification of the SINDCLUS algorithm for fitting the ADCLUS and INDCLUS models," Journal of Classification, Springer;The Classification Society, vol. 14(2), pages 297-310, September.
    8. Paolo Giordani & Henk Kiers, 2012. "FINDCLUS: Fuzzy INdividual Differences CLUStering," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 170-198, July.
    9. B. Mirkin, 1987. "Additive clustering and qualitative factor analysis methods for similarity matrices," Journal of Classification, Springer;The Classification Society, vol. 4(1), pages 7-31, March.
    10. 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.
    11. Michel Wedel & Wayne DeSarbo, 1998. "Mixtures of (constrained) ultrametric trees," Psychometrika, Springer;The Psychometric Society, vol. 63(4), pages 419-443, December.
    12. 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.
    13. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    14. Jan Schepers & Eva Ceulemans & Iven Mechelen, 2008. "Selecting Among Multi-Mode Partitioning Models of Different Complexities: A Comparison of Four Model Selection Criteria," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 67-85, June.
    15. Anil Chaturvedi & J. Carroll, 1994. "An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models," Journal of Classification, Springer;The Classification Society, vol. 11(2), pages 155-170, September.
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