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Distance Metrics and Clustering Methods for Mixed‐type Data

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  • Alexander H. Foss
  • Marianthi Markatou
  • Bonnie Ray

Abstract

In spite of the abundance of clustering techniques and algorithms, clustering mixed interval (continuous) and categorical (nominal and/or ordinal) scale data remain a challenging problem. In order to identify the most effective approaches for clustering mixed‐type data, we use both theoretical and empirical analyses to present a critical review of the strengths and weaknesses of the methods identified in the literature. Guidelines on approaches to use under different scenarios are provided, along with potential directions for future research.

Suggested Citation

  • Alexander H. Foss & Marianthi Markatou & Bonnie Ray, 2019. "Distance Metrics and Clustering Methods for Mixed‐type Data," International Statistical Review, International Statistical Institute, vol. 87(1), pages 80-109, April.
    • Handle: RePEc:bla:istatr:v:87:y:2019:i:1:p:80-109
    DOI: 10.1111/insr.12274
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    Cited by:

    1. Aurea Grané & Irene Albarrán & Roger Lumley, 2020. "Visualizing Inequality in Health and Socioeconomic Wellbeing in the EU: Findings from the SHARE Survey," IJERPH, MDPI, vol. 17(21), pages 1-18, October.
    2. Grané, Aurea & Salini, Silvia & Verdolini, Elena, 2021. "Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    3. Felix Mbuga & Cristina Tortora, 2021. "Spectral Clustering of Mixed-Type Data," Stats, MDPI, vol. 5(1), pages 1-11, December.
    4. Efthymios Costa & Ioanna Papatsouma & Angelos Markos, 2023. "Benchmarking distance-based partitioning methods for mixed-type data," 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. 17(3), pages 701-724, September.

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