Attribute value weighting in k-modes clustering

Zengyou He; Xiaofei Xu; Shengchun Deng

doi:10.1016/j.eswa.2011.06.027

Attribute value weighting in k-modes clustering

Zengyou He^*
, Xiaofei Xu
, Shengchun Deng

^*Corresponding author for this work

Faculty of Computing

Dalian University of Technology
Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

Abstract

In this paper, we generalize the k-modes clustering algorithm by weighting attribute value in the dissimilarity computation. Such a generalization generates clusters with stronger intra-similarities, leading to better clustering performance. Experimental results on real life data show that the new k-modes algorithm is superior to the standard k-modes algorithm with respect to clustering accuracy.

Original language	English
Pages (from-to)	15365-15369
Number of pages	5
Journal	Expert Systems with Applications
Volume	38
Issue number	12
DOIs	https://doi.org/10.1016/j.eswa.2011.06.027
State	Published - Nov 2011

Keywords

Categorical data
Clustering
Data mining
k-Means
k-Modes

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10.1016/j.eswa.2011.06.027

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@article{e88c161f9f914d20b2cd5ab4e94236cb,

title = "Attribute value weighting in k-modes clustering",

abstract = "In this paper, we generalize the k-modes clustering algorithm by weighting attribute value in the dissimilarity computation. Such a generalization generates clusters with stronger intra-similarities, leading to better clustering performance. Experimental results on real life data show that the new k-modes algorithm is superior to the standard k-modes algorithm with respect to clustering accuracy.",

keywords = "Categorical data, Clustering, Data mining, k-Means, k-Modes",

author = "Zengyou He and Xiaofei Xu and Shengchun Deng",

year = "2011",

month = nov,

doi = "10.1016/j.eswa.2011.06.027",

language = "英语",

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T1 - Attribute value weighting in k-modes clustering

AU - He, Zengyou

AU - Xu, Xiaofei

AU - Deng, Shengchun

PY - 2011/11

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N2 - In this paper, we generalize the k-modes clustering algorithm by weighting attribute value in the dissimilarity computation. Such a generalization generates clusters with stronger intra-similarities, leading to better clustering performance. Experimental results on real life data show that the new k-modes algorithm is superior to the standard k-modes algorithm with respect to clustering accuracy.

AB - In this paper, we generalize the k-modes clustering algorithm by weighting attribute value in the dissimilarity computation. Such a generalization generates clusters with stronger intra-similarities, leading to better clustering performance. Experimental results on real life data show that the new k-modes algorithm is superior to the standard k-modes algorithm with respect to clustering accuracy.

KW - Categorical data

KW - Clustering

KW - Data mining

KW - k-Means

KW - k-Modes

UR - https://www.scopus.com/pages/publications/80052037295

U2 - 10.1016/j.eswa.2011.06.027

DO - 10.1016/j.eswa.2011.06.027

M3 - 文章

AN - SCOPUS:80052037295

SN - 0957-4174

VL - 38

SP - 15365

EP - 15369

JO - Expert Systems with Applications

JF - Expert Systems with Applications

IS - 12

ER -

Attribute value weighting in k-modes clustering

Abstract

Keywords

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