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Choosing a Clustering: An A Posteriori Method for Social Networks
2019
Journal of Social Structure
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Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. ...
To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization ...
These methods may be better suited for positional analysis, since they are based on the tie profiles of the network actors. ...
doi:10.21307/joss-2019-022
fatcat:cjkcifylfzf55kn554vgmgku4y
Spatial analysis of three vegetation types in Xishuangbanna on a road network using the network K-function
2010
Procedia Environmental Sciences
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A new technique for analyzing the distribution of points on a network has been developed, called the network K-function for univariate analysis. ...
However the number of plantation forests increased and tend to cluster with the road networks. ...
Secondly, the calculation using SANET could spend quite lengthy computational time for some analyses (e.g., analysis of these points on this road network using a Pentium3 750 Mhz computer >4 hour). ...
doi:10.1016/j.proenv.2010.10.166
fatcat:lmlkuc3efvdvhc2md52pomypky
Scalability analysis of tightly-coupled FPGA-cluster for lattice Boltzmann computation
2012
22nd International Conference on Field Programmable Logic and Applications (FPL)
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Outline Lattice Boltzmann method (LBM) : method to compute fluid dynamics High parallelism Low operational intensity (each op. requires many data) For large-scale parallel computing.... ...
Limited scalability in large scale sys., conspicuous for strong scaling. caused by imbalanced performance and bandwidth. ...
Introduction
Lattice Boltzmann method (LBM)
FPGA-cluster for LBM
Performance model & analysis
Conclusions
LBM computation
FPGA cluster
Introduction
2012/9/5
3
FPGA-based Custom Computing ...
doi:10.1109/fpl.2012.6339275
dblp:conf/fpl/KonoSY12
fatcat:prbleqjdc5bzxg75goaqz4s4mm
Research on the Novel Computer Network Intrusion Detection Model based on Improved Particle Swarm Optimization Algorithm
2016
DEStech Transactions on Social Science Education and Human Science
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In this paper, we conduct research on the novel computer network intrusion detection model based on improved particle swarm optimization algorithm. ...
Under this basis, we propose the new perspective on the IDS system that will then enhance the robustness and safetiness of the overall network system. ...
Clustering method based on graph theory is used mostly point to the data to indicate the relationship between the data points, compared with other methods, this method is more suitable for the irregular ...
doi:10.12783/dtssehs/isetem2016/4386
fatcat:hgixrzsxtjcorcdug66ftri3v4
Machine Learning for Cyber Threat Detection
2020
International Journal of Advanced Trends in Computer Science and Engineering
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Use of social media and networking has increased in daily life, nowadays all are learning and working by using the internet but on the other hand, it becomes serious security threats problem. ...
However, it has difficult to use machine learning algorithms for threat detection analysis, due to huge number of negative threats detection, especially in the case of large scale environments. ...
Analysis of reports for anomaly detection The table below shows analysis of different algorithm based on various features i.e. reduced noise and computational cost, enhanced accuracy and performance. [ ...
doi:10.30534/ijatcse/2020/0891.12020
fatcat:ddb7mcjyxrhira5ghclt5a5bte
PRACTICAL NETWORK ANOMALY DETECTION USING DATA MINING TECHNIQUES
2016
VFAST Transactions on Software Engineering
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Network anomaly detection is an effective way to detect intrusions which defends our computer systems or network from attackers on the Internet. ...
In this paper, we introduce the current research works in network anomaly detection and consider several pratical solutions for this issue. ...
However the performance and computation complexity of KMean method are sensitive to the predefined number of clusters and initialized cluster centers. Wei et al. ...
doi:10.21015/vtse.v9i2.403
fatcat:wus2f2kxqfhz5hcritwals2evm
A Review on Clustering Technique
2015
International Journal on Recent and Innovation Trends in Computing and Communication
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Artificial Neural Network is very powerful tool in machine learning or in the field of computer visions. Competitive learning is used for Clustering in Neural network. ...
Example of Competitive learning, SOM and ART are famous for clustering. SOM have the limitation of dimension, ART is good but computation cost is very high. ...
It grows a cluster as long as, for each data point within this cluster, a neighborhood of a given radius contains at least a minimum number of points. DBSCAN has computational complexity O(n 2 ). ...
doi:10.17762/ijritcc2321-8169.1503136
fatcat:rdt7mpmyvnbm3ph6o7v72wmpdy
Towards Improved Detection of Intrusions with Constraint-Based Clustering (CBC)
2021
International Journal of Computer Networks And Applications
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Anomaly-based IDS is in the literature for the last few decades, but still the existing methods lack in three main aspectsdifficulty in handling mixed attribute types, more dependence on input parameters ...
a constraint-based clustering algorithm to closely learn the properties of normal connections. ...
A reference point is calculated for every partition and outliers are identified based on outlier rank computed with regard to the reference points. ...
doi:10.22247/ijcna/2021/207980
fatcat:isxile5rencsxflu7y7quvb4wi
A fast algorithm for constructing topological structure in large data
2012
Homology, Homotopy and Applications
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However, most methods developed so far are unsuitable for very large sets of data because of their computational difficulties. ...
The limitation of our method, as shown by our experiments, lies with the storage in the main memory rather than the computing time. ...
for their constructive discussions, suggestions and the useful information concerning the content of this paper. ...
doi:10.4310/hha.2012.v14.n1.a11
fatcat:jyyi6tqkzjaexmhvs3j34xt77i
An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks
2021
Journal of Sensors
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The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset ...
Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques ...
Acknowledgments The authors would like to acknowledge the support of Taif University Researchers Supporting Project number (TURSP-2020/239), Taif University, Taif, Saudi Arabia. ...
doi:10.1155/2021/5558860
fatcat:gjxl67tdlfadfjrdzkguk7rlfu
Integrative Learning for Population of Dynamic Networks with Covariates
2021
NeuroImage
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Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of ...
Extensive simulation studies illustrate sharp gains in recovering the true dynamic network over existing dynamic connectivity methods. ...
Acknowledgements The views expressed in this work do not necessarily reflect those of the National Institutes of Health, Department of Veterans Affairs or the United States Government. ...
doi:10.1016/j.neuroimage.2021.118181
pmid:34022384
pmcid:PMC8851385
fatcat:g6r3l43sqndqtd7aeahesrxe3i
Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China
2018
PLoS ONE
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The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. ...
This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. ...
The distance computation on the network is a basic operation for all the analysis methods in this work, but in slightly different ways. ...
doi:10.1371/journal.pone.0195093
pmid:29672551
pmcid:PMC5909624
fatcat:ynn6zxmlbje6ni2bkk3x33io4q
Effective trajectory data analysis using continuous k-means clustering
2016
International Journal of Latest Trends in Engineering and Technology
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K-means clustering algorithm is used to estimate the network load. All the network transactions are passed into the clustering process. Threshold values are used to find out the load differences. ...
The K-means clustering based monitoring model uses the centroid values to assess the network load difference. ...
Most of the monitoring tools use the protocol information and service details for the analysis. Statistical analysis is used for the network monitoring process. ...
doi:10.21172/1.72.547
fatcat:l4smz3dgcfcozd5tzw3mq55dp4
Review on EM-CURE Algorithm for Detection DDOS Attack
2018
International Journal Of Engineering And Computer Science
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The Entropy Method concept in term of windowing the incoming packets is applied with data mining technique using Clustering Using Representative (CURE) as cluster analysis to detect the DDoS attack in ...
on limited environments.DDoS attack detection very difficult because the non-existence of predefined rules to correctly identify the genuine network flow. ...
Treat each input point as separate cluster, compute u.closest for each u and then insert each cluster into the heap Q. ...
doi:10.18535/ijecs/v7i1.04
fatcat:i6zeexp2kjfypnczj6fdgmnife
CHALLENGES AND SOLUTIONS OF REAL-TIME CLUSTERING FOR NETWORK ANOMALY DETECTION
2019
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES
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However, cluster analysis precludes with it many challenges that need to be overcome for it to be adapted for real-time computation. ...
In unsupervised learning, inferences are obtained from input data without the need of any labeled response. Cluster analysis is one the most widely used algorithm for unsupervised learning. ...
greatly reduced for cluster analysis. ...
doi:10.26782/jmcms.2019.12.00078
fatcat:wffetv4xzfhkhkhaozzclsulza
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