Recent Developments in Multimode Clustering

  • Thomas Eckes
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


In recent years several models and corresponding algorithms for clustering two- or higher-mode data have been developed, including the additive-clustering approach (e.g., DeSarbo, 1982), the tree-fitting approach (e.g., De Soete & Carroll, 1989), and the error-variance approach (e.g., Eckes & Orlik, 1993). The present paper relates various types of data frequently collected in the behavioral and social sciences to prominent models of multimode clustering and demonstrates the versatility of three-mode clustering using a real data set drawn from social-psychological research.


Hierarchical Classis Proximity Data Disjoint Cluster Ultrametric Tree Less Square Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • Thomas Eckes
    • 1
  1. 1.Fachbereich GesellschaftswissenschaftenBergische Universität WuppertalWuppertalGermany

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