On the impact of dissimilarity measure in κ-modes clustering algorithm

Kwok Po NG; Mark Junjie Li; Joshua Zhexue Huang; Zengyou He

doi:10.1109/TPAMI.2007.53

On the impact of dissimilarity measure in κ-modes clustering algorithm

Kwok Po NG^*, Mark Junjie Li, Joshua Zhexue Huang, Zengyou He

^*Corresponding author for this work

Department of Mathematics

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

181 Citations (Scopus)

Abstract

This correspondence describes extensions to the κ-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.

Original language	English
Pages (from-to)	503-507
Number of pages	5
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	29
Issue number	3
DOIs	https://doi.org/10.1109/TPAMI.2007.53
Publication status	Published - Mar 2007

Scopus Subject Areas

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

User-Defined Keywords

κ-modes algorithm
Categorical data
Clustering
Data mining

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10.1109/TPAMI.2007.53

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title = "On the impact of dissimilarity measure in κ-modes clustering algorithm",

abstract = "This correspondence describes extensions to the κ-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.",

keywords = "κ-modes algorithm, Categorical data, Clustering, Data mining",

author = "NG, {Kwok Po} and Li, {Mark Junjie} and Huang, {Joshua Zhexue} and Zengyou He",

note = "Funding Information: This research was supported in part by RCG grant nos. 7046/03P, 7035/04P and 7035/05P and HKBU FRGs. Joshua Zhexue Huang research was supported in part by NSFC grants 60473091 and 60475026.",

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AU - Huang, Joshua Zhexue

AU - He, Zengyou

N1 - Funding Information: This research was supported in part by RCG grant nos. 7046/03P, 7035/04P and 7035/05P and HKBU FRGs. Joshua Zhexue Huang research was supported in part by NSFC grants 60473091 and 60475026.

PY - 2007/3

Y1 - 2007/3

N2 - This correspondence describes extensions to the κ-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.

AB - This correspondence describes extensions to the κ-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.

KW - κ-modes algorithm

KW - Categorical data

KW - Clustering

KW - Data mining

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M3 - Journal article

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SN - 0162-8828

VL - 29

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EP - 507

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

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ER -

On the impact of dissimilarity measure in κ-modes clustering algorithm

Abstract

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