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A Model for the Clustering of Variables Taking into Account External Data

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

Abstract

In this paper, a statistical model for the clustering of variables taking into account external data is proposed. This model is particularly appropriate for preference data in the presence of external information about the products. The clustering of variables around latent components (CLV method) is analysed on the basis of this model. Within the CLV method, there is one option without external data and one option taking into account external data. The criteria of both options can be expressed in function of the parameters of the postulated model. It is shown that the hierarchical algorithm finds the correct partition when the parameters of the model are known, no matter which option of CLV is used. Furthermore, the two options of CLV are compared by means of a simulation study. Both options perform well except for the case of small samples with a very large noise. Moreover, in most cases the performance of both options is equivalent.

Keywords

Covariance Matrix Parameter Vector Latent Component External Data Preference Data 
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.

References

  1. Sahmer, K. (2006). Propriétés et extensions de la classification de variables autour de composantes latentes. Application en évaluation sensorielle. Ph.D. thesis, Rennes, France/Dortmund, Germany.Google Scholar
  2. Vigneau, E., & Qannari, E. M. (2002). Segmentation of consumers taking account of external data. A clustering of variables approach. Food Quality and Preference, 13(7–8), 515–521.Google Scholar
  3. Vigneau, E., & Qannari, E. M. (2003). Clustering of variables around latent components. Communications in Statistics – Simulation and Computation, 32(4), 1131–1150.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Groupe ISALille CedexFrance

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