Indclus

INDCLUS: an individual differences generalization of the ADCLUS model and the MAPCLUS algorithm. We present a new model and associated algorithm, INDCLUS, that generalizes the Shepard-Arabie ADCLUS (ADditive CLUStering) model and the MAPCLUS algorithm, so as to represent in a clustering solution individual differences among subjects or other sources of data. Like MAPCLUS, the INDCLUS generalization utilizes an alternating least squares method combined with a mathematical programming optimization procedure based on a penalty function approach to impose discrete (0,1) constraints on parameters defining cluster membership. All subjects in an INDCLUS analysis are assumed to have a common set of clusters, which are differentially weighted by subjects in order to portray individual differences. As such, INDCLUS provides a (discrete) clustering counterpart to the Carroll-Chang INDSCAL model for (continuous) spatial representations. Finally, we consider possible generalizations of the INDCLUS model and algorithm.


References in zbMATH (referenced in 23 articles )

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  1. Bocci, Laura; Vicari, Donatella: ROOTCLUS: searching for “ROOT clusters” in three-way proximity data (2019)
  2. Bocci, Laura; Vicari, Donatella: GINDCLUS: generalized INDCLUS with external information (2017)
  3. France, Stephen L.; Chen, Wen; Deng, Yumin: ADCLUS and INDCLUS: analysis, experimentation, and meta-heuristic algorithm extensions (2017)
  4. Hansen, Pierre; Meyer, Christophe: A polynomial algorithm for a class of 0-1 fractional programming problems involving composite functions, with an application to additive clustering (2014)
  5. Blanchard, Simon J.; Desarbo, Wayne S.: A new zero-inflated negative binomial methodology for latent category identification (2013)
  6. Heiser, Willem J.: In memoriam, J. Douglas Carroll 1939--2011 (2013)
  7. Giordani, Paolo; Kiers, Henk A. L.: FINDCLUS: fuzzy individual differences clustering (2012)
  8. Heiser, Willem J.: In memoriam: J. Douglas Carroll, 1939--2011 (2012)
  9. Wilderjans, Tom F.; Depril, Dirk; Van Mechelen, Iven: Block-relaxation approaches for fitting the INDCLUS model (2012)
  10. Yokoyama, Satoru; Nakayama, Astudo; Okada, Akinori: One mode three-way overlapping cluster analysis (2009)
  11. Bocci, Laura; Vicari, Donatella; Vichi, Maurizio: A mixture model for the classification of three-way proximity data (2006)
  12. Leenen, Iwin; van Mechelen, Iven; De Boeck, Paul; Rosenberg, Seymour: INDCLAS: a three-way hierarchical classes model (1999)
  13. Vichi, Maurizio: Principal classifications analysis: a method for generating consensus dendrograms and its application to three-way data. (1998)
  14. Gordon, A. D.: A survey of constrained classification (1996)
  15. Carroll, J. Douglas; Corter, James E.: A graph-theoretic method for organizing overlapping clusters into trees, multiple trees, or extended trees (1995)
  16. Bove, G.; Di Ciaccio, A.: A user-oriented overview of multiway methods and software (1994)
  17. Gaul, Wolfgang; Schader, Martin: Pyramidal classification based on incomplete dissimilarity data (1994)
  18. Corter, James E.; Carroll, Douglas J.: Potential applications of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts. (1990) ioport
  19. Mirkin, Boris G.: A sequential fitting procedure for linear data analysis models (1990)
  20. Mirkin, B. G.: Additive clustering and qualitative factor analysis methods for similarity matrices (1987)

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