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2 results

  • 9 - Comparing Methods for the Evaluation of Cluster Structures in Multidimensional Analyses

  • from Part III - Perspectives on Multifactorial Methods
  • Edited by Ole Schützler, Universität Leipzig, Julia Schlüter
  • Book:
    Data and Methods in Corpus Linguistics
    Published online:
    06 May 2022
    Print publication:
    26 May 2022, pp 259-288

  • Summary

    This chapter sets out by discussing the way in which multidimensional techniques and visualizations have been used to analyse linguistic data. While, for instance, multidimensional scaling and unrooted phenograms (or NeighborNets) have primarily been designed for exploratory purposes, the author argues that they are in fact regularly used to put linguistic assumptions or hypotheses to the test. Cluster goodness (in terms of internal coherence and external distance from other clusters) in such approaches are typically evaluated based on a two-dimensional visualization. The author compares the affordances and limitations of visual inspection with a quantitative set of metrics that directly relates to visual displays but adds a degree of precision not attained by the human eye. The empirical part of the paper applies both approaches to a study of concessive constructions in six varieties of English, based on spoken and written material from the International Corpus of English. The author suggests that the new metrics can be usefully applied to a variety of multidimensional techniques to endow them with a measure of objectivity.

  • A comparison of heuristic and model-based clustering methods for dietary pattern analysis

  • Benjamin Greve, Iris Pigeot, Inge Huybrechts, Valeria Pala, Claudia Börnhorst
  • Journal:
    Public Health Nutrition / Volume 19 / Issue 2 / February 2016
    Published online by Cambridge University Press:
    20 January 2015, pp. 255-264
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  • 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.

    Design

    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.