Robust Model Design by Comparative Evaluation of Clustering Algorithms-Reference-Cited by-同舟云学术

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Robust Model Design by Comparative Evaluation of Clustering Algorithms

Published:2023 Issue: Volume:11 Page:88135-88151
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Chen Xiaopeng¹^ORCID,Park Chanseok¹^ORCID,Gao Xuehong²^ORCID,Kim Bosung³^ORCID

Affiliation:

1. Department of Industrial Engineering, Pusan National University, Busan, South Korea

2. Department of Safety Engineering, University of Science and Technology Beijing, Beijing, China

3. School of Management, Kyung Hee University, Seoul, South Korea

Funder

National Research Foundation of Korea (NRF) grants

Korean Government [Ministry of Science and Information and communications Technology (MSIT)]

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10223212.pdf?arnumber=10223212

Reference56 articles.

1. Robustness Properties of k Means and Trimmed k Means

2. Patch choice under perceptual constraints: a cause for departures from an ideal free distribution

3. Analysis and Implementation of an Adaptive Algorithm for the Rejection of Multiple Sinusoidal Disturbances

4. Robust deep k-means: An effective and simple method for data clustering

5. K-means-sharp: Modified centroid update for outlier-robust k-means clustering

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