SoPD -- A New Consensus Function for the Ensemble Clustering Problem

2012 ◽  
Author(s):  
Daniel Duarte Abdala ◽  
Xiaoyi Jiang
Keyword(s):  
Ensemble Clustering ◽  
Consensus Function ◽  
Clustering Problem

scholarly journals Converting Ensemble Clustering Problem to a Mathematical Optimization Problem and Providing an Approach to Solve Based on Optimization Toolbox

2018 ◽  
Author(s):  
Simintaj Salehpour ◽  
Hamid Parvin
Keyword(s):  
High Performance ◽  
Clustering Algorithms ◽  
Critical Role ◽  
Mathematical Optimization ◽  
Ensemble Clustering ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Lack Of Information ◽  
Stable Clusters ◽  
Information Retrieval Methods

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

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.

Identifying manufacturing operational conditions by physics-based feature extraction and ensemble clustering

2021 ◽  
Vol 60 ◽  
pp. 162-175
Author(s):  
Shenghan Guo ◽  
Mengfei Chen ◽  
Amir Abolhassani ◽  
Rajeev Kalamdani ◽  
Weihong Grace Guo
Keyword(s):  
Feature Extraction ◽  
Ensemble Clustering ◽  
Operational Conditions

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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