Recursive partitioning clustering tree algorithm

Ji Hoon Kang, Chan Hee Park, Seoung Bum Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it; (2) the final clustering result is represented by a set of if–then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.

Original languageEnglish
Pages (from-to)355-367
Number of pages13
JournalPattern Analysis and Applications
Volume19
Issue number2
DOIs
Publication statusPublished - 2016 May 1

Fingerprint

Clustering algorithms
Experiments

Keywords

  • Clustering algorithm
  • Recursive binary partitioning
  • Silhouette statistic
  • Unsupervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

Cite this

Recursive partitioning clustering tree algorithm. / Kang, Ji Hoon; Park, Chan Hee; Kim, Seoung Bum.

In: Pattern Analysis and Applications, Vol. 19, No. 2, 01.05.2016, p. 355-367.

Research output: Contribution to journalArticle

Kang, Ji Hoon ; Park, Chan Hee ; Kim, Seoung Bum. / Recursive partitioning clustering tree algorithm. In: Pattern Analysis and Applications. 2016 ; Vol. 19, No. 2. pp. 355-367.
@article{1d3d1f2aa96749e4bebb64a2328857a2,
title = "Recursive partitioning clustering tree algorithm",
abstract = "Clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it; (2) the final clustering result is represented by a set of if–then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.",
keywords = "Clustering algorithm, Recursive binary partitioning, Silhouette statistic, Unsupervised learning",
author = "Kang, {Ji Hoon} and Park, {Chan Hee} and Kim, {Seoung Bum}",
year = "2016",
month = "5",
day = "1",
doi = "10.1007/s10044-014-0399-1",
language = "English",
volume = "19",
pages = "355--367",
journal = "Pattern Analysis and Applications",
issn = "1433-7541",
publisher = "Springer London",
number = "2",

}

TY - JOUR

T1 - Recursive partitioning clustering tree algorithm

AU - Kang, Ji Hoon

AU - Park, Chan Hee

AU - Kim, Seoung Bum

PY - 2016/5/1

Y1 - 2016/5/1

N2 - Clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it; (2) the final clustering result is represented by a set of if–then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.

AB - Clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it; (2) the final clustering result is represented by a set of if–then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.

KW - Clustering algorithm

KW - Recursive binary partitioning

KW - Silhouette statistic

KW - Unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=84963655364&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963655364&partnerID=8YFLogxK

U2 - 10.1007/s10044-014-0399-1

DO - 10.1007/s10044-014-0399-1

M3 - Article

VL - 19

SP - 355

EP - 367

JO - Pattern Analysis and Applications

JF - Pattern Analysis and Applications

SN - 1433-7541

IS - 2

ER -