Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

Clustering on multilayer networks has been shown to be a promising approach to enhance the accuracy. Various multilayer networks clustering algorithms assume all networks derive from a latent clustering structure, and jointly learn the compatible and complementary information from different networks to excavate one shared underlying structure. However, such an assumption is in conflict with many emerging real-life applications due to the existence of noisy/irrelevant networks. To address this issue, we propose Centroid-based Multilayer Network Clustering (CMNC), a novel approach which can divide irrelevant relationships into different network groups and uncover the cluster structure in each group simultaneously. The multilayer networks is represented within a unified tensor framework for simultaneously capturing multiple types of relationships between a set of entities. By imposing the rank-(Lr,Lr,1) block term decomposition with nonnegativity, we are able to have well interpretations on the multiple clustering results based on graph cut theory. Numerically, we transform this tensor decomposition problem to an unconstrained optimization, thus can solve it efficiently under the nonlinear least squares (NLS) framework. Extensive experimental results on synthetic and real-world datasets show the effectiveness and robustness of our method against noise and irrelevant data.

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Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution

Bioinformatics ◽

10.1093/bioinformatics/btq655 ◽

2010 ◽

Vol 27 (3) ◽

pp. 326-333 ◽

Cited By ~ 25

Author(s):

Leonard Apeltsin ◽

John H. Morris ◽

Patricia C. Babbitt ◽

Thomas E. Ferrin

Keyword(s):

Weight Distribution ◽

Clustering Algorithms ◽

Edge Weight ◽

Network Clustering ◽

Similarity Network

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Complex Network Clustering Algorithms

Journal of Software ◽

10.3724/sp.j.1001.2009.00054 ◽

2009 ◽

Vol 20 (1) ◽

pp. 54-66 ◽

Cited By ~ 41

Author(s):

Bo YANG ◽

Da-You LIU ◽

Jiming LIU ◽

Di JIN ◽

Hai-Bin MA

Keyword(s):

Complex Network ◽

Clustering Algorithms ◽

Network Clustering

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A Relative Performance of Dissimilarity Measures for Matching Relational Web Access Patterns Between User Sessions

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Pattern Engineering System Development for Big Data Analytics ◽

10.4018/978-1-5225-3870-7.ch010 ◽

2018 ◽

pp. 153-176

Author(s):

Dilip Singh Sisodia

Keyword(s):

Clustering Algorithms ◽

Similarity Measures ◽

Comparative Performance ◽

Dissimilarity Measures ◽

User Session ◽

Relational Clustering ◽

Cluster Distance ◽

Web Access ◽

Access Patterns ◽

Cluster Quality

Customized web services are offered to users by grouping them according to their access patterns. Clustering techniques are very useful in grouping users and analyzing web access patterns. Clustering can be an object clustering performed on feature vectors or relational clustering performed on relational data. The relational clustering is preferred over object clustering for web users' sessions because of high dimensionality and sparsity of web users' data. However, relational clustering of web users depends on underlying dissimilarity measures used. Therefore, correct dissimilarity measure for matching relational web access patterns between user sessions is very important. In this chapter, the various dissimilarity measures used in relational clustering of web users' data are discussed. The concept of an augmented user session is also discussed to derive different augmented session dissimilarity measures. The discussed session dissimilarity measures are used with relational fuzzy clustering algorithms. The comparative performance binary session similarity and augmented session similarity measures are evaluated using intra-cluster and inter-cluster distance-based cluster quality ratio. The results suggested the augmented session dissimilarity measures in general, and intuitive augmented session (dis)similarity measure, in particular, performed better than the other measures.

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A Novel Complex Networks Clustering Algorithm Based on the Core Influence of Nodes

The Scientific World JOURNAL ◽

10.1155/2014/801854 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Chao Tong ◽

Jianwei Niu ◽

Bin Dai ◽

Zhongyu Xie

Keyword(s):

Complex Networks ◽

Clustering Algorithm ◽

Cluster Formation ◽

Clustering Algorithms ◽

Cluster Structure ◽

Network Clustering ◽

Clustering Methods ◽

Positive Role ◽

The Core ◽

Final Cluster

In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property. Network clustering methods are thus significant for the study of complex networks. Currently, many typical clustering algorithms have some weakness like inaccuracy and slow convergence. In this paper, we propose a clustering algorithm by calculating the core influence of nodes. The clustering process is a simulation of the process of cluster formation in sociology. The algorithm detects the nodes with core influence through their betweenness centrality, and builds the cluster’s core structure by discriminant functions. Next, the algorithm gets the final cluster structure after clustering the rest of the nodes in the network by optimizing method. Experiments on different datasets show that the clustering accuracy of this algorithm is superior to the classical clustering algorithm (Fast-Newman algorithm). It clusters faster and plays a positive role in revealing the real cluster structure of complex networks precisely.

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Network science meets respiratory medicine for OSAS phenotyping and severity prediction

PeerJ ◽

10.7717/peerj.3289 ◽

2017 ◽

Vol 5 ◽

pp. e3289 ◽

Cited By ~ 13

Author(s):

Stefan Mihaicuta ◽

Mihai Udrescu ◽

Alexandru Topirceanu ◽

Lucretia Udrescu

Keyword(s):

Sleep Apnea ◽

Classification Tree ◽

Clustering Algorithms ◽

Sleep Apnea Syndrome ◽

Network Clustering ◽

Obstructive Sleep ◽

Co Morbidity ◽

Severity Prediction ◽

Apnea Syndrome ◽

General Populations

Obstructive sleep apnea syndrome (OSAS) is a common clinical condition. The way that OSAS risk factors associate and converge is not a random process. As such, defining OSAS phenotypes fosters personalized patient management and population screening. In this paper, we present a network-based observational, retrospective study on a cohort of 1,371 consecutive OSAS patients and 611 non-OSAS control patients in order to explore the risk factor associations and their correlation with OSAS comorbidities. To this end, we construct the Apnea Patients Network (APN) using patient compatibility relationships according to six objective parameters: age, gender, body mass index (BMI), blood pressure (BP), neck circumference (NC) and the Epworth sleepiness score (ESS). By running targeted network clustering algorithms, we identify eight patient phenotypes and corroborate them with the co-morbidity types. Also, by employing machine learning on the uncovered phenotypes, we derive a classification tree and introduce a computational framework which render the Sleep Apnea Syndrome Score (SASScore); our OSAS score is implemented as an easy-to-use, web-based computer program which requires less than one minute for processing one individual. Our evaluation, performed on a distinct validation database with 231 consecutive patients, reveals that OSAS prediction with SASScorehas a significant specificity improvement (an increase of 234%) for only 8.2% sensitivity decrease in comparison with the state-of-the-art score STOP-BANG. The fact that SASScorehas bigger specificity makes it appropriate for OSAS screening and risk prediction in big, general populations.

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