Distance Metrics and Clustering Methods for Mixed‐type Data

Alexander H. Foss, Marianthi Markatou and Bonnie Ray

International Statistical Review, 2019, vol. 87, issue 1, 80-109

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.1111/insr.12274

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:istatr:v:87:y:2019:i:1:p:80-109

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0306-7734

Access Statistics for this article

International Statistical Review is currently edited by Eugene Seneta and Kees Zeelenberg

More articles in International Statistical Review from International Statistical Institute Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().