Journal of Classification

, Volume 29, Issue 3, pp 297–320

Lowdimensional Additive Overlapping Clustering

Authors

  • Dirk Depril
    • suAzio Consulting
  • Iven Van Mechelen
    • Faculty of Psychology and Educational SciencesKU Leuven
    • Faculty of Psychology and Educational SciencesKU Leuven
Article

DOI: 10.1007/s00357-012-9112-5

Cite this article as:
Depril, D., Van Mechelen, I. & Wilderjans, T.F. J Classif (2012) 29: 297. doi:10.1007/s00357-012-9112-5

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 clusteringDimensional reductionAlternating least squares algorithmTwo-way two-mode dataObject by variable data

Copyright information

© Springer Science+Business Media, LLC 2012