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Gaussian mixture modeling and model-based clustering under measurement inconsistency

Author

Listed:
  • Shuchismita Sarkar

    (Bowling Green State University)

  • Volodymyr Melnykov

    (The University of Alabama)

  • Rong Zheng

    (Western Illinois University)

Abstract

Finite mixtures present a powerful tool for modeling complex heterogeneous data. One of their most important applications is model-based clustering. It assumes that each data group can be reasonably described by one mixture model component. This establishes a one-to-one relationship between mixture components and clusters. In some cases, however, this relationship can be broken due to the presence of observations from the same class recorded in different ways. This effect can occur because of recording inconsistencies due to the use of different scales, operator errors, or simply various recording styles. The idea presented in this paper aims to alleviate this issue through modifications incorporated into mixture models. While the proposed methodology is applicable to a broad class of mixture models, in this paper it is illustrated on Gaussian mixtures. Several simulation studies and an application to a real-life data set are considered, yielding promising results.

Suggested Citation

  • Shuchismita Sarkar & Volodymyr Melnykov & Rong Zheng, 2020. "Gaussian mixture modeling and model-based clustering under measurement inconsistency," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 379-413, June.
    • Handle: RePEc:spr:advdac:v:14:y:2020:i:2:d:10.1007_s11634-020-00393-9
    DOI: 10.1007/s11634-020-00393-9
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    References listed on IDEAS

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