G-ANMI: A mutual information based genetic clustering algorithm for categorical data

Shengchun Deng; Zengyou He; Xiaofei Xu

doi:10.1016/j.knosys.2009.11.001

G-ANMI: A mutual information based genetic clustering algorithm for categorical data

Shengchun Deng
, Zengyou He^*
, Xiaofei Xu

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology
Hong Kong University of Science and Technology

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

49 Scopus citations

Abstract

Identification of meaningful clusters from categorical data is one key problem in data mining. Recently, Average Normalized Mutual Information (ANMI) has been used to define categorical data clustering as an optimization problem. To find globally optimal or near-optimal partition determined by ANMI, a genetic clustering algorithm (G-ANMI) is proposed in this paper. Experimental results show that G-ANMI is superior or comparable to existing algorithms for clustering categorical data in terms of clustering accuracy.

Original language	English
Pages (from-to)	144-149
Number of pages	6
Journal	Knowledge-Based Systems
Volume	23
Issue number	2
DOIs	https://doi.org/10.1016/j.knosys.2009.11.001
State	Published - Mar 2010

Keywords

Categorical data
Cluster ensemble
Clustering
Data mining
Genetic algorithm
Mutual information

Access to Document

10.1016/j.knosys.2009.11.001

Cite this

@article{d8dcbab583db4d2a8502a31214169349,

title = "G-ANMI: A mutual information based genetic clustering algorithm for categorical data",

abstract = "Identification of meaningful clusters from categorical data is one key problem in data mining. Recently, Average Normalized Mutual Information (ANMI) has been used to define categorical data clustering as an optimization problem. To find globally optimal or near-optimal partition determined by ANMI, a genetic clustering algorithm (G-ANMI) is proposed in this paper. Experimental results show that G-ANMI is superior or comparable to existing algorithms for clustering categorical data in terms of clustering accuracy.",

keywords = "Categorical data, Cluster ensemble, Clustering, Data mining, Genetic algorithm, Mutual information",

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

year = "2010",

month = mar,

doi = "10.1016/j.knosys.2009.11.001",

language = "英语",

volume = "23",

pages = "144--149",

journal = "Knowledge-Based Systems",

issn = "0950-7051",

publisher = "Elsevier B.V.",

number = "2",

}

TY - JOUR

T1 - G-ANMI

T2 - A mutual information based genetic clustering algorithm for categorical data

AU - Deng, Shengchun

AU - He, Zengyou

AU - Xu, Xiaofei

PY - 2010/3

Y1 - 2010/3

N2 - Identification of meaningful clusters from categorical data is one key problem in data mining. Recently, Average Normalized Mutual Information (ANMI) has been used to define categorical data clustering as an optimization problem. To find globally optimal or near-optimal partition determined by ANMI, a genetic clustering algorithm (G-ANMI) is proposed in this paper. Experimental results show that G-ANMI is superior or comparable to existing algorithms for clustering categorical data in terms of clustering accuracy.

AB - Identification of meaningful clusters from categorical data is one key problem in data mining. Recently, Average Normalized Mutual Information (ANMI) has been used to define categorical data clustering as an optimization problem. To find globally optimal or near-optimal partition determined by ANMI, a genetic clustering algorithm (G-ANMI) is proposed in this paper. Experimental results show that G-ANMI is superior or comparable to existing algorithms for clustering categorical data in terms of clustering accuracy.

KW - Categorical data

KW - Cluster ensemble

KW - Clustering

KW - Data mining

KW - Genetic algorithm

KW - Mutual information

UR - https://www.scopus.com/pages/publications/75849147143

U2 - 10.1016/j.knosys.2009.11.001

DO - 10.1016/j.knosys.2009.11.001

M3 - 文章

AN - SCOPUS:75849147143

SN - 0950-7051

VL - 23

SP - 144

EP - 149

JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

IS - 2

ER -

G-ANMI: A mutual information based genetic clustering algorithm for categorical data

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this