An optimization algorithm for clustering using weighted dissimilarity measures

Elaine Y. Chan; Wai Ki Ching; Michael K. Ng; Joshua Z. Huang

doi:10.1016/j.patcog.2003.11.003

An optimization algorithm for clustering using weighted dissimilarity measures

Elaine Y. Chan, Wai Ki Ching, Michael K. Ng^*, Joshua Z. Huang

^*Corresponding author for this work

Department of Mathematics

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

234 Citations (Scopus)

Abstract

One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets.

Original language	English
Pages (from-to)	943-952
Number of pages	10
Journal	Pattern Recognition
Volume	37
Issue number	5
DOIs	https://doi.org/10.1016/j.patcog.2003.11.003
Publication status	Published - May 2004

Scopus Subject Areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

User-Defined Keywords

Attributes weights
Clustering
Data mining
Optimization

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10.1016/j.patcog.2003.11.003

Cite this

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title = "An optimization algorithm for clustering using weighted dissimilarity measures",

abstract = "One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets.",

keywords = "Attributes weights, Clustering, Data mining, Optimization",

author = "Chan, {Elaine Y.} and Ching, {Wai Ki} and Ng, {Michael K.} and Huang, {Joshua Z.}",

note = "Funding Information: The research is supported in part by Hong Kong Research Grants Council Grant Nos. HKU 7126/02P, HKU 7130/02P and HKU 7046/03P. Publisher copyright: {\textcopyright} 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.",

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T1 - An optimization algorithm for clustering using weighted dissimilarity measures

AU - Chan, Elaine Y.

AU - Ching, Wai Ki

AU - Ng, Michael K.

AU - Huang, Joshua Z.

N1 - Funding Information: The research is supported in part by Hong Kong Research Grants Council Grant Nos. HKU 7126/02P, HKU 7130/02P and HKU 7046/03P. Publisher copyright: © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

PY - 2004/5

Y1 - 2004/5

N2 - One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets.

AB - One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets.

KW - Attributes weights

KW - Clustering

KW - Data mining

KW - Optimization

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DO - 10.1016/j.patcog.2003.11.003

M3 - Journal article

AN - SCOPUS:1842762839

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VL - 37

SP - 943

EP - 952

JO - Pattern Recognition

JF - Pattern Recognition

IS - 5

ER -

An optimization algorithm for clustering using weighted dissimilarity measures

Abstract

Scopus Subject Areas

User-Defined Keywords

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