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Journal of Classification
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02-04-2019

Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering

Authors: Zdeněk Šulc, Hana Řezanková

Published in: Journal of Classification | Issue 1/2019

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Abstract

This paper deals with similarity measures for categorical data in hierarchical clustering, which can deal with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures consider additional characteristics of a dataset, such as a frequency distribution of categories or the number of categories of a given variable. The paper recognizes two main aims. First, to compare and evaluate the selected similarity measures regarding the quality of produced clusters in hierarchical clustering. Second, to propose new similarity measures for nominal variables. All the examined similarity measures are compared regarding the quality of the produced clusters using the mean ranked scores of two internal evaluation coefficients. The analysis is performed on the generated datasets, and thus, it allows determining in which particular situations a certain similarity measure is recommended for use.
previous article A Mixture of Coalesced Generalized Hyperbolic Distributions
next article Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering

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Metadata
Title
Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering
Authors
Zdeněk Šulc
Hana Řezanková
Publication date
02-04-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2019
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09317-5

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Optimal Landmark Point Selection Using Clustering for Manifold Modeling and Data Classification

Multiclass Classification Based on Multi-criteria Decision-making

Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering

Growth Mixture Modeling with Measurement Selection

Robustification of Gaussian Bayes Classifier by the Minimum β-Divergence Method

A New Method for Classifying Random Variables Based on Support Vector Machine

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