A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm

The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships be...

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Detalles Bibliográficos
Autores principales: Zhou, Hongfang, Zhang, Yihui, Liu, Yibin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5387825/
https://www.ncbi.nlm.nih.gov/pubmed/28458686
http://dx.doi.org/10.1155/2017/3691316
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author Zhou, Hongfang
Zhang, Yihui
Liu, Yibin
author_facet Zhou, Hongfang
Zhang, Yihui
Liu, Yibin
author_sort Zhou, Hongfang
collection PubMed
description The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao's algorithms.
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spelling pubmed-53878252017-04-30 A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm Zhou, Hongfang Zhang, Yihui Liu, Yibin Comput Intell Neurosci Research Article The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao's algorithms. Hindawi 2017 2017-03-28 /pmc/articles/PMC5387825/ /pubmed/28458686 http://dx.doi.org/10.1155/2017/3691316 Text en Copyright © 2017 Hongfang Zhou et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhou, Hongfang
Zhang, Yihui
Liu, Yibin
A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
title A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
title_full A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
title_fullStr A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
title_full_unstemmed A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
title_short A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
title_sort global-relationship dissimilarity measure for the k-modes clustering algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5387825/
https://www.ncbi.nlm.nih.gov/pubmed/28458686
http://dx.doi.org/10.1155/2017/3691316
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