Multi-label Learning with Label-Specific Features via Clustering Ensemble

2017 ◽  
Author(s):  
Wang Zhan ◽  
Min-Ling Zhang
Keyword(s):  
Clustering Ensemble

Clustering ensemble method based on co-occurrence similarity

2011 ◽  
Vol 31 (2) ◽  
pp. 441-445 ◽  
Author(s):  
Guang LING ◽  
Ming-chun WANG ◽  
Jia-yi FENG
Keyword(s):  
Ensemble Method ◽  
Clustering Ensemble

Hybrid genetic model for clustering ensemble

2021 ◽  
pp. 107457
Author(s):  
Wenlu Yang ◽  
Yinghui Zhang ◽  
Hongjun Wang ◽  
Ping Deng ◽  
Tianrui Li
Keyword(s):  
Genetic Model ◽  
Clustering Ensemble

Clustering Ensemble for Prioritized Sampling Based on Average and Rough Patterns

2017 ◽  
pp. 530-539 ◽  
Author(s):  
Matt Triff ◽  
Ilya Pavlovski ◽  
Zhixing Liu ◽  
Lori-Anne Morgan ◽  
Pawan Lingras
Keyword(s):  
Clustering Ensemble

scholarly journals Spectral Clustering Ensemble Applied to SAR Image Segmentation

2008 ◽  
Vol 46 (7) ◽  
pp. 2126-2136 ◽  
Author(s):  
Xiangrong Zhang ◽  
Licheng Jiao ◽  
Fang Liu ◽  
Liefeng Bo ◽  
Maoguo Gong
Keyword(s):  
Image Segmentation ◽  
Spectral Clustering ◽  
Sar Image ◽  
Clustering Ensemble ◽  
Sar Image Segmentation

Nonnegative matrix factorization for clustering ensemble based on dark knowledge

2019 ◽  
Vol 163 ◽  
pp. 624-631 ◽  
Author(s):  
Wenting Ye ◽  
Hongjun Wang ◽  
Shan Yan ◽  
Tianrui Li ◽  
Yan Yang
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Clustering Ensemble

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

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