Adapted algorithm of choosing initial values for k-means document clustering

Yuanchao Liu; Xiaolong Wang; Bingquan Liu

Adapted algorithm of choosing initial values for k-means document clustering

Yuanchao Liu^*
, Xiaolong Wang
, Bingquan Liu

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

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

8 Scopus citations

Abstract

In this paper, a novel algorithm of choosing initial values for k-means document clustering is proposed, which is based on an adapted minimum maximum principle. Firstly similarity matrix is constructed, and then an adapted minimum maximum principle is used to select both the initial seeds and the value of k. The experiment results show that the value of k found by this method is very near to the true value.

Original language	English
Pages (from-to)	11-15
Number of pages	5
Journal	Gaojishu Tongxin/Chinese High Technology Letters
Volume	16
Issue number	1
State	Published - Jan 2006
Externally published	Yes

Keywords

Document clustering
Minimum maximum principle
Similarity matrix
k-means

Cite this

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title = "Adapted algorithm of choosing initial values for k-means document clustering",

abstract = "In this paper, a novel algorithm of choosing initial values for k-means document clustering is proposed, which is based on an adapted minimum maximum principle. Firstly similarity matrix is constructed, and then an adapted minimum maximum principle is used to select both the initial seeds and the value of k. The experiment results show that the value of k found by this method is very near to the true value.",

keywords = "Document clustering, Minimum maximum principle, Similarity matrix, k-means",

author = "Yuanchao Liu and Xiaolong Wang and Bingquan Liu",

year = "2006",

month = jan,

language = "英语",

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journal = "Gaojishu Tongxin/Chinese High Technology Letters",

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T1 - Adapted algorithm of choosing initial values for k-means document clustering

AU - Liu, Yuanchao

AU - Wang, Xiaolong

AU - Liu, Bingquan

PY - 2006/1

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N2 - In this paper, a novel algorithm of choosing initial values for k-means document clustering is proposed, which is based on an adapted minimum maximum principle. Firstly similarity matrix is constructed, and then an adapted minimum maximum principle is used to select both the initial seeds and the value of k. The experiment results show that the value of k found by this method is very near to the true value.

AB - In this paper, a novel algorithm of choosing initial values for k-means document clustering is proposed, which is based on an adapted minimum maximum principle. Firstly similarity matrix is constructed, and then an adapted minimum maximum principle is used to select both the initial seeds and the value of k. The experiment results show that the value of k found by this method is very near to the true value.

KW - Document clustering

KW - Minimum maximum principle

KW - Similarity matrix

KW - k-means

UR - https://www.scopus.com/pages/publications/33646520126

M3 - 文章

AN - SCOPUS:33646520126

SN - 1002-0470

VL - 16

SP - 11

EP - 15

JO - Gaojishu Tongxin/Chinese High Technology Letters

JF - Gaojishu Tongxin/Chinese High Technology Letters

IS - 1

ER -

Adapted algorithm of choosing initial values for k-means document clustering

Abstract

Keywords

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