A note on constrained k-means algorithms

Michael K. Ng

doi:10.1016/S0031-3203(99)00057-6

A note on constrained k-means algorithms

Michael K. Ng^*

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Journal article › peer-review

70 Citations (Scopus)

Abstract

This paper describes extensions to the k-means algorithm for clustering data sets. By adding suitable constraints into the mathematical program formulation, an approach is developed, which allows the use of the k-means paradigm to efficiently cluster data sets with the fixed number of objects in each cluster. The new algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.

Original language	English
Pages (from-to)	515-519
Number of pages	5
Journal	Pattern Recognition
Volume	33
Issue number	3
DOIs	https://doi.org/10.1016/S0031-3203(99)00057-6
Publication status	Published - Mar 2000

User-Defined Keywords

Clustering
Constraints
k-means algorithm
PCB insertion

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10.1016/S0031-3203(99)00057-6

Cite this

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title = "A note on constrained k-means algorithms",

abstract = "This paper describes extensions to the k-means algorithm for clustering data sets. By adding suitable constraints into the mathematical program formulation, an approach is developed, which allows the use of the k-means paradigm to efficiently cluster data sets with the fixed number of objects in each cluster. The new algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.",

keywords = "Clustering, Constraints, k-means algorithm, PCB insertion",

author = "Ng, \{Michael K.\}",

note = "Funding Information: Research by Michael Ng was supported in part by HKU CRCG Grant nos. 10201824 and 10201939. Publisher copyright: {\textcopyright} 2000 Pattern Recognition Society. Published by Elsevier B.V. All rights reserved.",

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AU - Ng, Michael K.

N1 - Funding Information: Research by Michael Ng was supported in part by HKU CRCG Grant nos. 10201824 and 10201939. Publisher copyright: © 2000 Pattern Recognition Society. Published by Elsevier B.V. All rights reserved.

PY - 2000/3

Y1 - 2000/3

N2 - This paper describes extensions to the k-means algorithm for clustering data sets. By adding suitable constraints into the mathematical program formulation, an approach is developed, which allows the use of the k-means paradigm to efficiently cluster data sets with the fixed number of objects in each cluster. The new algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.

AB - This paper describes extensions to the k-means algorithm for clustering data sets. By adding suitable constraints into the mathematical program formulation, an approach is developed, which allows the use of the k-means paradigm to efficiently cluster data sets with the fixed number of objects in each cluster. The new algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.

KW - Clustering

KW - Constraints

KW - k-means algorithm

KW - PCB insertion

UR - https://www.scopus.com/pages/publications/0033909225

U2 - 10.1016/S0031-3203(99)00057-6

DO - 10.1016/S0031-3203(99)00057-6

M3 - Journal article

AN - SCOPUS:0033909225

SN - 0031-3203

VL - 33

SP - 515

EP - 519

JO - Pattern Recognition

JF - Pattern Recognition

IS - 3

ER -

A note on constrained k-means algorithms

Abstract

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