Please use this identifier to cite or link to this item:
Title: Hierarchical clustering of mixed-type data based on barycentric coding
Authors: Moschidis, Odysseas
Markos, Angelos
Chadjipadelis, Theodore
Type: Article
Subjects: FRASCATI::Social sciences
FRASCATI::Social sciences
Keywords: Ward clustering
Chi-square distance
Subjects MESH: Hierarchical cluster analysis
Mixed-type data
Issue Date: 2023
Source: Behaviormetrika
Volume: 50
Issue: 1
First Page: 465
Last Page: 489
Abstract: Clustering of mixed-type datasets can be a particularly challenging task as it requires taking into account the associations between variables with different level of measurement, i.e., nominal, ordinal and/or interval. In some cases, hierarchical clustering is considered a suitable approach, as it makes few assumptions about the data and its solution can be easily visualized. Since most hierarchical clustering approaches assume variables are measured on the same scale, a simple strategy for clustering mixed-type data is to homogenize the variables before clustering. This would mean either recoding the continuous variables as categorical ones or vice versa. However, typical discretization of continuous variables implies loss of information. In this work, an agglomerative hierarchical clustering approach for mixed-type data is proposed, which relies on a barycentric coding of continuous variables. The proposed approach minimizes information loss and is compatible with the framework of correspondence analysis. The utility of the method is demonstrated on real and simulated data.
ISSN: 0385-7417
Other Identifiers: 10.1007/s41237-022-00165-z
Appears in Collections:Department of Business Administration

Files in This Item:
File Description SizeFormat 
manusc.pdf620,46 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.