Parallel Clustering Search Applied to Capacitated Centered Clustering Problem

2019 ◽  
Author(s):  
Davi Melo Morales ◽  
Antonio Augusto Chaves ◽  
Alvaro Luiz Fazenda
Keyword(s):  
Clustering Problem ◽  
Clustering Search ◽  
Parallel Clustering

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 Solution strategy based on Gaussian mixture models and dispersion reduction for the capacitated centered clustering problem

2021 ◽  
Vol 7 ◽  
pp. e332
Author(s):  
Santiago-Omar Caballero-Morales
Keyword(s):  
Mixture Models ◽  
Variable Neighborhood Search ◽  
Gaussian Mixture Models ◽  
Distribution Patterns ◽  
Gaussian Mixture ◽  
Neighborhood Search ◽  
Solution Strategy ◽  
Clustering Problem ◽  
Simulated Annealing Genetic Algorithm ◽  
Clustering Search

The Capacitated Centered Clustering Problem (CCCP)—a multi-facility location model—is very important within the logistics and supply chain management fields due to its impact on industrial transportation and distribution. However, solving the CCCP is a challenging task due to its computational complexity. In this work, a strategy based on Gaussian mixture models (GMMs) and dispersion reduction is presented to obtain the most likely locations of facilities for sets of client points considering their distribution patterns. Experiments performed on large CCCP instances, and considering updated best-known solutions, led to estimate the performance of the GMMs approach, termed as Dispersion Reduction GMMs, with a mean error gap smaller than 2.6%. This result is more competitive when compared to Variable Neighborhood Search, Simulated Annealing, Genetic Algorithm and CKMeans and faster to achieve when compared to the best-known solutions obtained by Tabu-Search and Clustering Search.

Multi-Start Jaya Algorithm for Software Module Clustering Problem

2018 ◽  
Vol 1 (1) ◽  
pp. 87-112 ◽  
Author(s):  
Kamal Z. Zamli ◽  
◽  
Abdulrahman Alsewari ◽  
Bestoun S. Ahmed ◽  
◽  
...  
Keyword(s):  
Jaya Algorithm ◽  
Software Module ◽  
Clustering Problem ◽  
Software Module Clustering

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

Sixteen Open Clusters Discovered with Sample-based Clustering Search of Gaia DR2

2020 ◽  
Vol 132 (1009) ◽  
pp. 034502 ◽  
Author(s):  
ChaoJie Hao ◽  
Ye Xu ◽  
ZhenYu Wu ◽  
ZhiHong He ◽  
ShuaiBo Bian
Keyword(s):  
Open Clusters ◽  
Clustering Search ◽  
Gaia Dr2

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.

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