title: A comparison of heuristic and model-based clustering methods for dietary pattern analysis.
abstract: OBJECTIVE
  Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data.
  
  
  METHODS
  The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice.
  
  
  RESULTS
  The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster.
  
  
  CONCLUSIONS
  The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.
authors.affiliation: 1Leibniz-Institute for Prevention Research and Epidemiology - BIPS GmbH,Achterstrasse 30,28359 Bremen,Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Greve
authors.affiliation: 1Leibniz-Institute for Prevention Research and Epidemiology - BIPS GmbH,Achterstrasse 30,28359 Bremen,Germany.
authors.forename: Iris
authors.initials: I
authors.lastname: Pigeot
authors.affiliation: 3Department of Public Health,Ghent University,Ghent,Belgium.
authors.forename: Inge
authors.initials: I
authors.lastname: Huybrechts
authors.affiliation: 5Department of Preventive & Predictive Medicine,Fondazione IRCCS Istituto Nazionale dei Tumori,Milan,Italy.
authors.forename: Valeria
authors.initials: V
authors.lastname: Pala
authors.affiliation: 1Leibniz-Institute for Prevention Research and Epidemiology - BIPS GmbH,Achterstrasse 30,28359 Bremen,Germany.
authors.forename: Claudia
authors.initials: C
authors.lastname: Börnhorst
mesh_terms: D002648:Child; D016000:Cluster Analysis; D004032:Diet; D004034:Diet Surveys; D005247:Feeding Behavior; D000066506:Heuristics; D006801:Humans; D007223:Infant; D012107:Research Design
keywords:     k-means; Gaussian mixture model; IDEFICS study; Multidimensional data; Ward’s minimum variance method