Clustering search algorithm for the capacitated centered clustering problem

2010 ◽  
Vol 37 (3) ◽  
pp. 552-558 ◽  
Author(s):  
Antonio Augusto Chaves ◽  
Luiz Antonio Nogueira Lorena
Keyword(s):  
Search Algorithm ◽  
Clustering Problem ◽  
Clustering Search

scholarly journals Clustering of cancer data based on Stiefel manifold for multiple views

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jing Tian ◽  
Jianping Zhao ◽  
Chunhou Zheng
Keyword(s):  
Optimization Problem ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Stiefel Manifold ◽  
Omics Data ◽  
K Nearest Neighbor ◽  
Cancer Data ◽  
Clustering Problem ◽  
Multiple Datasets ◽  
Cluster Class

Abstract Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

A tabu search algorithm for a capacitated clustering problem

2018 ◽  
Vol 33 (3) ◽  
pp. 387 ◽  
Author(s):  
Sudha Khambhampati ◽  
Prasad Calyam ◽  
Xinhui Zhang
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Clustering Problem

scholarly journals A spectral clustering search algorithm for predicting shallow landslide size and location

2015 ◽  
Vol 120 (2) ◽  
pp. 300-324 ◽  
Author(s):  
Dino Bellugi ◽  
David G. Milledge ◽  
William E. Dietrich ◽  
Jim A. McKean ◽  
J. Taylor Perron ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Search Algorithm ◽  
Shallow Landslide ◽  
Clustering Search

scholarly journals Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views

2021 ◽  
Author(s):  
Jing Tian ◽  
Jianping Zhao ◽  
Chun-hou Zheng
Keyword(s):  
Nearest Neighbor ◽  
Search Algorithm ◽  
Small Sample Size ◽  
Small Sample ◽  
Stiefel Manifold ◽  
K Nearest Neighbor ◽  
Cancer Data ◽  
Clustering Problem ◽  
Multiple Datasets ◽  
Cluster Class

Abstract Background: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of these datasets has proved to be valuable for biological and medical research and helpful to reveal data structures from multiple collections. However, such data often have small sample size and high dimension. It is difficult to find a suitable integration method for structural analysis of multiple datasets. Results: In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, the optimization problem solved by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same.Conclusion: Particularly, our approach has better performs when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

scholarly journals Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm

2015 ◽  
Vol 120 (12) ◽  
pp. 2552-2585 ◽  
Author(s):  
Dino Bellugi ◽  
David G. Milledge ◽  
William E. Dietrich ◽  
J. Taylor Perron ◽  
Jim McKean
Keyword(s):  
Spectral Clustering ◽  
Search Algorithm ◽  
Shallow Landslide ◽  
Natural Landscape ◽  
Clustering Search
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