|Table of Contents|

Weighted k-means Algorithm Based on Outlier Detection(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年01期
Page:
75-80
Research Field:
机器学习
Publishing date:

Info

Title:
Weighted k-means Algorithm Based on Outlier Detection
Author(s):
Hu Haojie1; Chen Hui2; Mu Tingting3; Yao Minli1; He Fang1; Zhang Fenggan1
(1.Rocket Force Engineering University,Xi’an 710025,China)(2.The Fourth Academy of China Aerospace Science and Technology Corporation,Xi’an 710025,China)(3.Beijing New Era Global Import and Export Co.,Ltd.,Beijing 100027,China)
Keywords:
clustering; k-means; outlier detection; 0-norm
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2022.01.011
Abstract:
In this paper,to solve the problem of that few outliers can easily destroy the cluster structure,leading to a significant deviation for the obtained centroids in k-means clustering algorithm,we assign different weights on the data points based on their distance from the potential cluster center to alleviate the negative impact on the data structure. Moreover,we also incorporate outlier detection in our clustering model by imposing 0-norm constraint on weight assignments. To optimize the model,we introduce an efficient alternating minimization algorithm. Extensive experiments on both synthetic and real datasets show the effectiveness of the proposed model.

References:

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Last Update: 2022-03-15