A Robust k-Means Clustering Algorithm Based on Observation Point Mechanism
The k-means algorithm is sensitive to the outliers. In this paper, we propose a robust two-stage k-means clustering algorithm based on the observation point mechanism, which can accurately discover the cluster centers without the disturbance of outliers. In the first stage, a small subset of the ori...
Saved in:
Main Authors: | Xiaoliang Zhang, Yulin He, Yi Jin, Honglian Qin, Muhammad Azhar, Joshua Zhexue Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2020/3650926 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Label iteration-based clustering ensemble algorithm
by: HE Yulin, et al.
Published: (2024-12-01) -
Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm
by: Yanhai Yang, et al.
Published: (2020-01-01) -
Improved Honey Badger Algorithm and Its Application to K-Means Clustering
by: Shuhao Jiang, et al.
Published: (2025-01-01) -
Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms
by: Yeliz Karaca, et al.
Published: (2018-01-01) -
K-Means Clustering with Local Distance Privacy
by: Mengmeng Yang, et al.
Published: (2023-12-01)