Journal of Classification

, Volume 29, Issue 3, pp 297–320

Lowdimensional Additive Overlapping Clustering

  • Dirk Depril
  • Iven Van Mechelen
  • Tom F. Wilderjans
Article

Abstract

To reveal the structure underlying two-way two-mode object by variable data, Mirkin (1987) has proposed an additive overlapping clustering model. This model implies an overlapping clustering of the objects and a reconstruction of the data, with the reconstructed variable profile of an object being a summation of the variable profiles of the clusters it belongs to. Grasping the additive (overlapping) clustering structure of object by variable data may, however, be seriously hampered in case the data include a very large number of variables. To deal with this problem, we propose a new model that simultaneously clusters the objects in overlapping clusters and reduces the variable space; as such, the model implies that the cluster profiles and, hence, the reconstructed data profiles are constrained to lie in a lowdimensional space. An alternating least squares (ALS) algorithm to fit the new model to a given data set will be presented, along with a simulation study and an illustrative example that makes use of empirical data.

Keywords

Additive overlapping clustering Dimensional reduction Alternating least squares algorithm Two-way two-mode data Object by variable data 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Dirk Depril
    • 1
  • Iven Van Mechelen
    • 2
  • Tom F. Wilderjans
    • 2
  1. 1.suAzio ConsultingAntwerpBelgium
  2. 2.Faculty of Psychology and Educational SciencesKU LeuvenLeuvenBelgium

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