Distance Metrics and Clustering Methods for Mixed-type Data

Foss, Alexander H.; Markatou, Marianthi; Ray, Bonnie

doi:10.1111/insr.12274

Title: Distance Metrics and Clustering Methods for Mixed-type Data

Journal Article · 20 June 2018 · International Statistical Review

DOI: https://doi.org/10.1111/insr.12274 · OSTI ID:1459931

^[1]; Markatou, Marianthi ^[1]; Ray, Bonnie ^[2]

Univ. at Buffalo, Buffalo, NY (United States)
Arenadotio, New York, NY (United States)

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. Here, the guidelines on approaches to use under different scenarios are provided, along with potential directions for future research.

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1459931

Report Number(s):: SAND--2018-7091J; {665360,"Journal ID: ISSN 0306-7734"}

Journal Information:: International Statistical Review, Journal Name: International Statistical Review Journal Issue: 1 Vol. 87; ISSN 0306-7734

Country of Publication:: United States

Language:: English

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Title: Distance Metrics and Clustering Methods for Mixed-type Data

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