scholarly journals A Statistical Performance Analysis of Graph Clustering Algorithms

2018 ◽  
pp. 170-184 ◽  
Author(s):  
Pierre Miasnikof ◽  
Alexander Y. Shestopaloff ◽  
Anthony J. Bonner ◽  
Yuri Lawryshyn
Keyword(s):  
Performance Analysis ◽  
Clustering Algorithms ◽  
Graph Clustering

Performance analysis for clustering algorithms

2016 ◽  
Vol 7 (5) ◽  
pp. 485 ◽  
Author(s):  
Yu Xue ◽  
Binping Zhao ◽  
Tinghuai Ma
Keyword(s):  
Performance Analysis ◽  
Clustering Algorithms

scholarly journals Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1271
Author(s):  
Hoyeon Jeong ◽  
Yoonbee Kim ◽  
Yi-Sue Jung ◽  
Dae Ryong Kang ◽  
Young-Rae Cho
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Graph Clustering ◽  
Functional Modules ◽  
Protein Protein Interactions ◽  
Overlapping Clusters ◽  
Novel Proteins ◽  
Ppi Networks ◽  
Function Modules

Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

scholarly journals Metric-Constrained Optimization for Graph Clustering Algorithms

2019 ◽  
Vol 1 (2) ◽  
pp. 333-355 ◽  
Author(s):  
Nate Veldt ◽  
David F. Gleich ◽  
Anthony Wirth ◽  
James Saunderson
Keyword(s):  
Constrained Optimization ◽  
Clustering Algorithms ◽  
Graph Clustering

Clustering Algorithms for Outlier Detection Performance Analysis

2015 ◽  
Vol 4 (1) ◽  
pp. 36-39
Author(s):  
S. Vijayarani ◽  
◽  
S. Maria Sylviaa ◽  
A. Sakila ◽  
◽  
...  
Keyword(s):  
Performance Analysis ◽  
Outlier Detection ◽  
Clustering Algorithms ◽  
Detection Performance

MANET-Based Stable Clustering Algorithm and its Performance Analysis

2014 ◽  
Vol 571-572 ◽  
pp. 100-104
Author(s):  
Guo Zhao Hou ◽  
Jin Biao Wang ◽  
Jing Wu
Keyword(s):  
Performance Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Stability ◽  
Average Variation ◽  
Quantitative Analyses ◽  
Cluster Maintenance

In MANET, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a MANET-based stable clustering algorithm (MSCA) was proposed. Firstly, MSCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that MSCA can improve cluster stability and reduce clustering overheads.

Sign in / Sign up

Export Citation Format

Share Document