Weighting variables in K-means clustering

被引：13

作者：

Huh, Myung-Hoe ^{[2
]}

Lim, Yong B. ^{[1
]}

机构：

[1] Ewha Womans Univ, Dept Stat, Seoul, South Korea

[2] Korea Univ, Dept Stat, Seoul, South Korea

来源：

JOURNAL OF APPLIED STATISTICS | 2009年 / 36卷 / 01期

关键词：

K-means clustering; variable weighting; penalty constant;

D O I：

10.1080/02664760802382533

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The aim of this study is to assign weights w1, , wm to m clustering variables Z1, , Zm, so that k groups were uncovered to reveal more meaningful within-group coherence. We propose a new criterion to be minimized, which is the sum of the weighted within-cluster sums of squares and the penalty for the heterogeneity in variable weights w1, , wm. We will present the computing algorithm for such k-means clustering, a working procedure to determine a suitable value of penalty constant and numerical examples, among which one is simulated and the other two are real.

引用

页码：67 / 78

页数：12

共 50 条

[41] PCA-Guided k-Means with Variable Weighting and Its Application to Document Clustering
Honda, Katsuhiro
Notsu, Akira
Ichihashi, Hidetomo
MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2009, 5861 : 282 - 292
[42] Document Clustering Using K-Means with Term Weighting as Similarity-Based Constraints
Buatoom, Uraiwan
Kongprawechnon, Waree
Theeramunkong, Thanaruk
SYMMETRY-BASEL, 2020, 12 (06):
[43] Using K-Means Clustering and Parameter Weighting for Partial-Discharge Noise Suppression
Lin, Yu-Hsun
IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (04) : 2380 - 2390
[44] Soil data clustering by using K-means and fuzzy K-means algorithm
Hot, Elma
Popovic-Bugarin, Vesna
2015 23RD TELECOMMUNICATIONS FORUM TELFOR (TELFOR), 2015, : 890 - 893
[45] Empirical Evaluation of K-Means, Bisecting K-Means, Fuzzy C-Means and Genetic K-Means Clustering Algorithms
Banerjee, Shreya
Choudhary, Ankit
Pal, Somnath
2015 IEEE INTERNATIONAL WIE CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (WIECON-ECE), 2015, : 172 - 176
[46] k*-means:: A new generalized k-means clustering algorithm
Cheung, YM
PATTERN RECOGNITION LETTERS, 2003, 24 (15) : 2883 - 2893
[47] K*-Means: An Effective and Efficient K-means Clustering Algorithm
Qi, Jianpeng
Yu, Yanwei
Wang, Lihong
Liu, Jinglei
PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCES ON BIG DATA AND CLOUD COMPUTING (BDCLOUD 2016) SOCIAL COMPUTING AND NETWORKING (SOCIALCOM 2016) SUSTAINABLE COMPUTING AND COMMUNICATIONS (SUSTAINCOM 2016) (BDCLOUD-SOCIALCOM-SUSTAINCOM 2016), 2016, : 242 - 249
[48] An Improved K-means Clustering Algorithm
Wang Yintong
Li Wanlong
Gao Rujia
2012 WORLD AUTOMATION CONGRESS (WAC), 2012,
[49] Unsupervised K-Means Clustering Algorithm
Sinaga, Kristina P.
Yang, Miin-Shen
IEEE ACCESS, 2020, 8 : 80716 - 80727
[50] Dynamic Incremental K-means Clustering
Aaron, Bryant
Tamir, Dan E.
Rishe, Naphtali D.
Kandel, Abraham
2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), VOL 1, 2014, : 308 - 313

← 1 2 3 4 5 →