Distance Metrics and Clustering Methods for Mixed‐type Data

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Abstract
Summary: 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.
Funding Information
  • Patient-Centered Outcomes Research Institute (PCORI) (IHS-150731640)

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