Applicability of Network Clustering Methods for Risk Analysis

2009 ◽  
pp. 283-306 ◽  
Author(s):  
M. Sanchez-Silva
Keyword(s):  
Risk Analysis ◽  
Network Clustering ◽  
Clustering Methods

scholarly journals Hospital-Nursing Home Transfer Patterns and Influence on Nursing Home Clostridium difficile Infection Rates

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S402-S403
Author(s):  
Lauren Campbell ◽  
Kristen Bush ◽  
Ghinwa Dumyati
Keyword(s):  
Nursing Home ◽  
Network Analysis ◽  
Online Survey ◽  
Negative Binomial ◽  
Network Clustering ◽  
Emerging Infections ◽  
Clustering Methods ◽  
Data Set ◽  
Patient Transfers ◽  
Network Analyses

Abstract Background Little is known as to how hospital C. difficile infection (CDI) may impact nursing home (NH) CDI, or how patient transfers may modify this relationship. This study aims to examine a possible association between hospital and NH CDI rates, and whether NH CDI rates are influenced by patient transfers from hospital to NH. Methods Patient transfers among the 5 hospitals and 34 NHs in Monroe County, NY were identified from the Minimum Data Set (MDS) 3.0 and Medicare Provider Analysis and Review files for 2011–13, and aggregated to the NH level. NH and hospital CDI rates were obtained from Emerging Infections Program CDI population surveillance and National Healthcare Safety Network data, respectively. Multivariate negative binomial regression modeled the association between hospital CDI rate (weighted by hospital-to-NH transfers/overall transfers among hospitals and NHs) and NH CDI rate, controlling for NH covariates from NH Compare and the Online Survey, Certification, and Reporting files. Patient transfer networks between hospitals and NHs were constructed, and basic network analysis of transfer patterns was conducted to confirm contributing factors to NH CDI rates from the multivariate model. Results When weighted hospital CDI rate increased by 1%, NH CDI rate increased by 18% (P = 0.016). Antibiotic and feeding tube prevalence were associated with a 4% and 8% increase in NH CDI rate, respectively (P≤0.014). Network analysis confirmed multivariate results and detected hospital-NH pairs with high edge weights (number of transfers) where NHs receiving patients from hospitals with high CDI rates had higher CDI rates. Network clustering methods were used to identify 2 sub-networks within overall annual networks and clusters of hospital-NH pairs for targeted intervention. Conclusion Hospital CDI rate, adjusting for patient transfers, is associated with higher NH CDI rates in multivariate and network analyses, suggesting that NHs with a large inflow of patients from hospitals may need to implement stricter infection prevention practices to reduce transmission among residents. By identifying regional sub-networks, network analysis can also be used to actively manage facility CDI and prevent spread to other healthcare facilities. Disclosures All authors: No reported disclosures.

scholarly journals A review on data clustering using spiking neural network (SNN) models

2019 ◽  
Vol 15 (3) ◽  
pp. 1392
Author(s):  
Siti Aisyah Mohamed ◽  
Muhaini Othman ◽  
Mohd Hafizul Afifi
Keyword(s):  
Neural Network ◽  
Data Clustering ◽  
Network Clustering ◽  
Complex Data ◽  
Spiking Neural Network ◽  
Clustering Methods ◽  
Clustering Method ◽  
Static Data ◽  
Clustering Approach ◽  
Clustering Problems

The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to discover and understand complex data.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
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.

scholarly journals “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Qin Wu ◽  
Xingqin Qi ◽  
Eddie Fuller ◽  
Cun-Quan Zhang
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Network Clustering ◽  
Clustering Methods ◽  
Social Network Data ◽  
Centrality Score ◽  
Vertex Centrality ◽  
Guided Clustering

Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering.

scholarly journals Comprehension and Thinking of Complex Network Construction and Clustering Based on Neural Network Algorithm

2018 ◽  
Vol 173 ◽  
pp. 03040
Author(s):  
Bing Shen
Keyword(s):  
Neural Network ◽  
Complex Networks ◽  
Complex Network ◽  
Small World ◽  
Network Clustering ◽  
Clustering Methods ◽  
Development History ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Application Fields

With the development of computer technology and the enhancement of people's cognition of the world, more and more scholars have been focusing on the research of complex networks. At the same time, the emerging machine learning neural network algorithm has become a powerful tool for various researchers. This paper mainly discusses the construction and clustering of complex networks based on neural network algorithm. Firstly, the development history and main application fields of neural network are introduced. Then, several common methods of complex network clustering are summarized, and then the limitations of these clustering methods are discussed. At last, it proposes to improve the construction of neural network through the concept of small world in complex network and enhance the effect of complex network clustering by the characteristics of neural network algorithm, including the accuracy, reliability, stability, speed, etc.

The integration of surface seismic and borehole data using artificial neural network clustering methods

2001 ◽  
Vol 2001 (1) ◽  
pp. 1-4
Author(s):  
Matthew Carr ◽  
Richard Cooper ◽  
Maggie Smith ◽  
M. Turhan Taner ◽  
Joel Walls
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Clustering ◽  
Clustering Methods ◽  
Borehole Data ◽  
Artificial Neural

scholarly journals A Bayesian Multivariate Mixture Model for Spatial Transcriptomics Data

2021 ◽  
Author(s):  
Carter Allen ◽  
Yuzhou Chang ◽  
Brian Neelon ◽  
Won Chang ◽  
Hang Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mixture Model ◽  
Data Augmentation ◽  
Tissue Sample ◽  
Expression Profiles ◽  
Mixture Component ◽  
Network Clustering ◽  
Clustering Methods ◽  
Tissue Samples ◽  
Distinct Cell

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations within a tissue sample that reflect distinct cell types or states. Existing methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya–Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment.

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