Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering-Reference-Cited by-同舟云学术

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Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering

Published:2024-08 Issue: Volume:161 Page:111712
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Golzari Oskouei Amin,Samadi Negin^ORCID,Tanha Jafar

Publisher

Elsevier BV

Reference65 articles.

1. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects;Ezugwu;Eng. Appl. Artif. Intell.,2022

2. Clustering with implicit constraints: a novel approach to housing market segmentation;Zhang;Trans. GIS,2022

3. Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation;Zhang;Pattern Recognit.,2022

4. A review on semi-supervised clustering;Cai;Inf. Sci.,2023

5. A novel intuitionistic fuzzy c-means clustering algorithm and its application to medical images;Chaira;Appl. Soft Comput.,2011

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SSFCM-FWCW: Semi-Supervised Fuzzy C-Means method based on Feature-Weight and Cluster-Weight learning;Software Impacts;2024-09

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