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检索条件"主题词=Clusteringalgorithms"

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A fuzzy clustering algorithm to detect criminals without prior information

A fuzzy clustering algorithm to detect criminals without pri...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Fan, Changjun Xiao, Kaiming Xiu, Baoxin Lv, Guodong Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479958771

Crime analysis has been widely studied, but problem of identifying conspirators through communication network analysis is still not well resolved. In this paper, we proposed a fuzzy clustering algorithm to detect hidden criminals from topic network, which took no use of individuals' prior identity information. We first built up a local suspicion calculation from nodes' neighboring information (node and edge);and then with global information, we employed the fuzzy k-means clustering algorithm, and made the membership to suspicious group as the global suspicion degree. Experiments showed it works well on identification: known suspects gained relative high values and known innocents got relative low values.

关键词： Algorithmdesignandanalysis Analyticalmodels clusteringalgorithms Conferences Indexes Semantics Socialnetworkservices Criminals Semantics Clustering algorithms Fuzzy semiotics Suspicion

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Clique Migration: Affinity Grouping of Virtual Machines for Inter-Cloud Live Migration 9

Clique Migration: Affinity Grouping of Virtual Machin...

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9th IEEE International Conference on Networking, Architecture, and Storage (NAS)

作者： Lu, Tao Stuart, Morgan Tang, Kun He, Xubin Virginia Commonwealth Univ Dept Elect & Comp Engn Richmond VA 23284 USA

ISBN: (纸本)9781479940875

Affinity is common among Virtual Machines (VMs) in cloud environments. If VMs collaborating on a job are split in geographically distributed clouds, the low bandwidth and high latency inter-cloud communication via a wide area network (WAN) will dramatically degrade the system performance. A potential solution is migrating all of the VMs collaborating on a job in parallel, so as to avoid wide area communication. However, if the job is too large, it becomes impractical to migrate all of the VMs simultaneously due to limited WAN bandwidth and high block dirty rate. We propose a migration optimization mechanism called Clique Migration to partition a large group of VMs into subgroups based on the traffic affinities among VMs. Then, subgroups are migrated one at a time. Based on Clique Migration, we propose and implement two algorithms called R-Min-Cut and Kmeans-SF. Analysis of the traffic trace of 68 VMs in an IBM production cluster shows that our algorithms can reduce inter-cloud traffic by 25% to 60%, when the degree of parallel migration is from 2 to 32. Tests of MPI multi-PingPing benchmark running on simulated inter-cloud environments, show that our algorithms can significantly shorten the period during which applications undergo performance degradation. Tests of MPI Reduce scatter benchmark show that R-Min-Cut can keep the performance during migration at 26% to 75% of the non-migration scenario.

关键词： Algorithmdesignandanalysis Bandwidth clusteringalgorithms Degradation Partitioningalgorithms Production Wideareanetworks Inter-Cloud Livemigration Traffic-awaregrouping Virtualmachine Wideareanetwork Bandwidth Immigration Migrations jobs Wide area networks virtual machines affinity Affine transformation message passing mannose phosphate isomerase remote procedure calls deterioration degradation of performance

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A Novel Algorithm for Community Detection and Influence Ranking in Social Networks

A Novel Algorithm for Community Detection and Influence Rank...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Wang, Wenjun Street, W. Nick Univ Iowa Dept Management Sci Iowa City IA 52242 USA

ISBN: (纸本)9781479958771

Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail how the node's influence is distributed through the network. Similarity in this influence space defines a new, meaningful and refined connectivity measure for the closeness of any pair of nodes. Our approach not only differentiates the influence ranking but also effectively finds communities in both undirected and directed networks, and incorporates these two important tasks into one integrated framework. We demonstrate its superior performance with extensive tests on a set of real-world networks and synthetic benchmarks.

关键词： Algorithmdesignandanalysis Benchmarktesting clusteringalgorithms Communities Conferences Socialnetworkservices Vectors Social Networks Social Capital impact assessment Network topology Ganglia Communities Cloning Vectors Intimacy

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Comparison of Various Improved-Partition Fuzzy c-Means Clustering Algorithms in Fast Color Reduction 12

Comparison of Various Improved-Partition Fuzzy c-Mean...

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12th IEEE International Symposium on Intelligent Systems and Informatics (SISY)

作者： Szilagyi, Laszlo Denesi, Gellert Kovacs, Levente Szilagyi, Sandor M. Budapest Univ Technol & Econ Dept Control Engn & Informat Technol H-1117 Budapest Hungary Sapientia Univ Dept Elect Engn Targu Mures 540485 Romania Obuda Univ Appl Informat Inst John von Neumann Fac Informat Budapest Hungary Petru Maior Univ Dept Informat Targu Mures 540088 Romania

ISBN: (纸本)9781479959969

This paper provides a comparative study of several enhanced versions of the fuzzy c-means clustering algorithm in an application of histogram-based image color reduction. A common preprocessing is performed before clustering, consisting of a preliminary color quantization, histogram extraction and selection of frequently occurring colors of the image. These selected colors will be clustered by tested c-means algorithms. Clustering is followed by another common step, which creates the output image. Besides conventional hard (HCM) and fuzzy c-means (FCM) clustering, the so-called generalized improved partition FCM algorithm, and several versions of the suppressed FCM (s-FCM) in its conventional and generalized form, are included in this study. Accuracy is measured as the average color difference between pixels of the input and output image, while efficiency is mostly characterized by the total runtime of the performed color reduction. Numerical evaluation found all enhanced FCM algorithms more accurate, and four out of seven enhanced algorithms faster than FCM. All tested algorithms can create reduced color images of acceptable quality.

关键词： Accuracy clusteringalgorithms Imagecoloranalysis Partitioningalgorithms Prototypes Quantization(signal) Vectors PROTOTYPE Color CHROMATIC ABERRATION Clustering algorithms fast color algorithms Fuzzy color image Imagery (Psychotherapy) Image images Cloning Vectors Cluster Analysis

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Overlapping Stochastic Community Finding

Overlapping Stochastic Community Finding

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： McDaid, Aaron Hurley, Neil Murphy, Brendan Univ Coll Dublin Sch Comp Sci & Informat Insight Ctr Data Analyt Dublin 2 Ireland Univ Coll Dublin Sch Math Sci Insight Ctr Data Analyt Dublin 2 Ireland

ISBN: (纸本)9781479958771

Community finding in social network analysis is the task of identifying groups of people within a larger population who are more likely to connect to each other than connect to others in the population. Much existing research has focussed on non-overlapping clustering. However, communities in real-world social networks do overlap. This paper introduces a new community finding method based on overlapping clustering. A Bayesian statistical model is presented, and a Markov Chain Monte Carlo (MCMC) algorithm is presented and evaluated in comparison with two existing overlapping community finding methods that are applicable to large networks. We evaluate our algorithm on networks with thousands of nodes and tens of thousands of edges.

关键词： clusteringalgorithms Communities Computationalmodeling Educationalinstitutions Proposals Socialnetworkservices Stochasticprocesses FINDINGS new communities overlaps Communities Social network analysis Markov chain monte carlo methods

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How Do Biological Networks Differ from Social Networks? (An Experimental Study)

How Do Biological Networks Differ from Social Networks? (An ...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Gutierrez-Bunster, Tatiana Stege, Ulrike Thomo, Alex Taylor, John Univ Victoria Dept Comp Sci Victoria BC Canada Univ Victoria Dept Biol Victoria BC V8W 2Y2 Canada Univ Bio Bio Dept Sistemas Informac Concepcion Chile

ISBN: (纸本)9781479958771

In this paper we outline important differences between (1) protein interaction networks and (2) social and other complex networks, in terms of fine-grained network community profiles. While these families of networks present some general similarities, they also have some stark differences in the way the communities are formed. Namely, we find that the sizes of the best communities in such biological networks are an order of magnitude smaller than in social and other complex networks. We furthermore find that the generative model describing biological networks is very different from the model describing social networks. While for latter the Forest-Fire model best approximates their network community profile, for biological networks it is a random rewiring model that generates networks with the observed profiles. Our study suggests that these families of networks should be treated differently when deriving results from network analysis, and a fine-grained analysis is needed to better understand their structure.

关键词： Biologicalsystemmodeling clusteringalgorithms Communities Conferences Proteins Socialnetworkservices

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Using Total Correlation to Discover Related Clusters of Clinical Chemistry Parameters 12

Using Total Correlation to Discover Related Clusters of Clin...

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12th IEEE International Symposium on Intelligent Systems and Informatics (SISY)

作者： Ferenci, Tamas Kovacs, Levente Obuda Univ Physiol Controls Grp Budapest Hungary

ISBN: (纸本)9781479959969

Clinical chemistry tests are widely used in medical diagnosis. Physicians typically interpret them in a univariate sense, by comparing each parameter to a reference interval, however, their correlation structure may also be interesting, as it can shed light on common physiologic or pathological mechanisms. The correlation analysis of such parameters is hindered by two problems: the relationships between the variables are sometimes non-linear and of unknown functional form, and the number of such variables is high, making the use of classical tools infeasible. This paper presents a novel approach to address both problems. It uses an information theory-based measure called total correlation to quantify the dependence between clinical chemistry variables, as total correlation can detect any dependence between the variables, non-linear or even non-monotone ones as well, hence it is completely insensitive to the actual nature of the relationship. Another advantage is that is can quantify dependence not only between pairs of variables, but between larger groups of variables as well. By the virtue of this fact, a novel approach is presented that can handle the high dimensionality of clinical chemistry parameters. The approach is implemented and illustrated on a real-life database from the representative US public health survey NHANES.

关键词： Chemistry clusteringalgorithms Correlation Databases Entropy Joints Mutualinformation clinical chemistry Database Joints Joints entropy Chemistry Clinical Chemistry Tests

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CluClas: Hybrid Clustering-Classification Approach for Accurate and Efficient Network Classification 39

CluClas: Hybrid Clustering-Classification Approach fo...

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39th Annual IEEE Conference on Local Computer Networks (LCN)

作者： Fahad, Adil Alharthi, Kurayman Tari, Zahir Almalawi, Abdulmohsen Khalil, Ibrahim RMIT Univ Sch Comp Sci & IT Melbourne Vic Australia

ISBN: (纸本)9781479937806

The traffic classification is the foundation for many network activities, such as Quality of Service (QoS), security monitoring, Lawful Interception and Intrusion Detection Systems (IDS). A recent statistics-based approach to address the unsatisfactory results of traditional port-based and payload-based approaches has attracted attention. However, the presence of non-informative attributes and noise instances degrade the performance of this approach. Thus, to address this problem, in this paper, we propose a hybrid clustering-classification approach (called CluClas) to improve the accuracy and efficiency of network traffic classification by selecting informative attributes and representative instances. An extensive empirical study on four traffic data sets shows the effectiveness of our proposed approach.

关键词： Accuracy clusteringalgorithms Datamodels HiddenMarkovmodels Measurement Testing Training Functional training traffic classification Quality of service Training HYBRID Hybrid electric vehicles safety monitoring INTRUSION DETECTION SYSTEMS

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Overlapping community detection in large networks from a data fusion view

Overlapping community detection in large networks from a dat...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Yu, Le Wu, Bin Zhao, Shuai Wang, Bai Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100088 Peoples R China BUPT State Key Lab Networking & Switching Beijing Peoples R China

ISBN: (纸本)9781479958771

Community detection is one of the most important problems in social network analysis in the context of the structure of the underlying graphs. Many researchers have proposed their own methods for discovering dense regions in social networks. Such methods are only designed with links of the underlying social network. However, with the development of recent applications, rich edge content can be available to give another view to the community detection process. In this study, we focus on improving community detection with the edge content in social networks. In order to regulate the effect of both linkage structure and edge content, we propose two feature integration strategies. Experiment results illustrate that the presence of edge content provides unprecedented opportunities and flexibility for the community detection process.

关键词： Algorithmdesignandanalysis clusteringalgorithms Communities Conferences Imageedgedetection Socialnetworkservices Vectors

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Empirical Study on Overlapping Community Detection in Question and Answer Sites

Empirical Study on Overlapping Community Detection in Questi...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Meng, Zide Gandon, Fabien Zucker, Catherine Faron Song, Ge INRIA Sophia Antipolis Mediterranee F-06900 Sophia Antipolis France Univ Nice Sophia Antipolis CNRS UMR 7271 I3S F-06900 Sophia Antipolis France

ISBN: (纸本)9781479958771

In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and help us find interest groups. Identifying these social communities can bring benefit to understanding and predicting users behaviors. However, for some kind of online community sites such as question-and-answer (Q&A) sites or forums, there is no friendship based social network structure, which means people are not aware who they are in contact with. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose an empirical approach for extracting data from Q&A sites suitable to apply community detection methods. Then we compare three kinds of community detection methods we applied on a dataset extracted from the popular Q&A site StackOverflow. We analyze and comment the results of each method.

关键词： Cascadingstylesheets clusteringalgorithms Communities HTML Layout Socialnetworkservices Vegetation HyperText Markup Language layouts vegetation Quality assurance extracting data Communities empirical studies Interest groups empirical approach Friendship inspection methods NETWORK STRUCTURE Social Networks

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