Bibliographies thématiques / Module clustering

Littérature scientifique sur le sujet « Module clustering »

Auteur : Grafiati
Publié le 17 janvier 2024

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Articles de revues sur le sujet "Module clustering"

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Alshareef, Haya, et Mashael Maashi. « Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem ». Applied Sciences 12, no 11 (2 juin 2022) : 5649. http://dx.doi.org/10.3390/app12115649.

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Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreover, multi-objective software module clustering involves assembling a specific group of modules according to specific cluster criteria. Software module clustering classifies software modules into different clusters to enhance the software maintenance process. A structure with low coupling and high cohesion is considered an excellent software module structure. In this study, we apply a multi-objective hyper-heuristic method to solve the multi-objective module clustering problem with three objectives: (i) minimize coupling, (ii) maximize cohesion, and (iii) ensure high modularization quality. We conducted several experiments to obtain optimal and near-optimal solutions for the multi-objective module clustering optimization problem. The experimental results demonstrated that the HHMO_CF_GDA method outperformed the individual multi-objective evolutionary algorithms in solving the multi-objective software module clustering optimization problem. The resulting software, in which HHMO_CF_GDA was applied, was more optimized and achieved lower coupling with higher cohesion and better modularization quality. Moreover, the structure of the software was more robust and easier to maintain because of its software modularity.
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Hou, Jie, Xiufen Ye, Chuanlong Li et Yixing Wang. « K-Module Algorithm : An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks ». Genes 12, no 1 (12 janvier 2021) : 87. http://dx.doi.org/10.3390/genes12010087.

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Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance correlation for WGCNA to construct a gene co-expression network, and present a new approach called the k-module algorithm to improve the WGCNA clustering results. This method can assign all genes to the module with the highest mean connectivity with these genes. This algorithm re-adjusts the results of hierarchical clustering while retaining the advantages of the dynamic tree cut method. The validity of the algorithm is verified using six datasets from microarray and RNA-seq data. The k-module algorithm has fewer iterations, which leads to lower complexity. We verify that the gene modules obtained by the k-module algorithm have high enrichment scores and strong stability. Our method improves upon hierarchical clustering, and can be applied to general clustering algorithms based on the similarity matrix, not limited to gene co-expression network analysis.
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Hu, Hai Yan, et You Qiao Zhang. « The Study and Realization of Energy-Aware Routing Algorithm of Wireless Sensor Networks ». Applied Mechanics and Materials 201-202 (octobre 2012) : 767–72. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.767.

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Based on the analysis of routing algorithm of typical wireless sensor networks, the author puts forward with the objectives of routing algorithm and designs energy-aware routing algorithm to reduce energy consumption and extend life cycle of the whole network. The algorithm constitutes four modules: clustering module, dynamic cluster head election module, dormant state module and inter-cluster routing module. Aiming at effectively using the energy of sensor nodes, the paper makes use of honeycomb-like two-level clustering structure to increase coverage rate of nodes. Also, studies of routing are discussed on the two aspects, being the inter-clustering dynamic cluster head election and introduction of dormant mechanism, and secondly, the inter-clustering reliable routing.The routing algorithm has its prototype realized and effectively verified on the test bed provided by the laboratory.
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Kirve, Shraddha. « Clustering Techniques in Wireless Sensor Networks : A Practical Study ». International Journal for Research in Applied Science and Engineering Technology 9, no VI (10 juin 2021) : 536–38. http://dx.doi.org/10.22214/ijraset.2021.34990.

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Our Solution for the Mentioned Problem Statement Comprised of Different Modules such as Alert &Notification Module, Real-Time Data Collection Module from Authenticated Source, Precaution Module to Define and Broadcast Protocol to Disaster Affected Areas, Social Media Message Circulation (SMMC) Module. IENS (Indian Early Notification System) has been designed by our team to Get & Fetch Notification System as soon as Disaster Stuck or Popped-Up (Introduce/Originated) and notifies as well as channelize Related Information via Different Social Media Official Platforms.
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Karayiannis, Dimitrios, et Spyros Tragoudas. « Clustering Network Modules with Different Implementations for Delay Minimization ». VLSI Design 7, no 1 (1 janvier 1998) : 1–13. http://dx.doi.org/10.1155/1998/69289.

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In recent years there has been an extensive interest in clustering the modules of a network so that the maximum delay from any primary input to any primary output is minimized [8, 7, 6]. Clusters have a maximum capacity and modules may have different implementations. All existing CAD frameworks initially select an implementation of each module, and at a later stage they cluster the modules. We present an approach that clusters the nodes, while considering their alternative implementations, so that we further minimize the maximum delay after the clustering. Our approach is based on optimal algorithms for restricted versions of this complex problem in circuit design, and outperforms the conventional approach, which first obtains an implementation for each circuit module without considering clustering and then, in a later step, performs clustering.
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Mohammad Shahid, Sunil Gupta et MS. Sofia Pillai. « Machine Learning-Based False Positive Software Vulnerability Analysis ». Global Journal of Innovation and Emerging Technology 1, no 1 (15 juin 2022) : 29–35. http://dx.doi.org/10.58260/j.iet.2202.0105.

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Measurements and fault data from an older software version were used to build the fault prediction model for the new release. When past fault data isn't available, it's a problem. The software industry's assessment of programme module failure rates without fault labels is a difficult task. Unsupervised learning can be used to build a software fault prediction model when module defect labels are not available. These techniques can help identify programme modules that are more prone to errors. One method is to make use of clustering algorithms. Software module failures can be predicted using unsupervised techniques such as clustering when fault labels are not available. Machine learning clustering-based software failure prediction is our approach to solving this complex problem.
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Strauch, Martin, Jochen Supper, Christian Spieth, Dierk Wanke, Joachim Kilian, Klaus Harter et Andreas Zell. « A Two-Step Clustering for 3-D Gene Expression Data Reveals the Main Features of the Arabidopsis Stress Response ». Journal of Integrative Bioinformatics 4, no 1 (1 mars 2007) : 81–93. http://dx.doi.org/10.1515/jib-2007-54.

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Summary We developed an integrative approach for discovering gene modules, i.e. genes that are tightly correlated under several experimental conditions and applied it to a threedimensional Arabidopsis thaliana microarray dataset. The dataset consists of approximately 23000 genes responding to 9 abiotic stress conditions at 6-9 different points in time. Our approach aims at finding relatively small and dense modules lending themselves to a specific biological interpretation. In order to detect gene modules within this dataset, we employ a two-step clustering process. In the first step, a k-means clustering on one condition is performed, which is subsequently used in the second step as a seed for the clustering of the remaining conditions. To validate the significance of the obtained modules, we performed a permutation analysis and determined a null hypothesis to compare the module scores against, providing a p-value for each module. Significant modules were mapped to the Gene Ontology (GO) in order to determine the participating biological processes.As a result, we isolated modules showing high significance with respect to the p-values obtained by permutation analysis and GO mapping. In these modules we identified a number of genes that are either part of a general stress response with similar characteristics under different conditions (coherent modules), or part of a more specific stress response to a single stress condition (single response modules). We also found genes clustering within several conditions, which are, however, not part of a coherent module. These genes have a distinct temporal response under each condition. We call the modules they are contained in individual response modules (IR).
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Yu, Limin, Xianjun Shen, Jincai Yang, Kaiping Wei, Duo Zhong et Ruilong Xiang. « Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module ». Evolutionary Bioinformatics 16 (janvier 2020) : 117693432097057. http://dx.doi.org/10.1177/1176934320970572.

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Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI .
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Alam, M. K., Azrina Abd Aziz, S. A. Latif et Azlan Awang. « Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks ». Sensors 20, no 4 (13 février 2020) : 1011. http://dx.doi.org/10.3390/s20041011.

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A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.
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Wu, Yong Liang, Bao Quan Mao, Li Xu, Dong Ming Dai et Yan Chao Liu. « The Evaluation of Module Division Programme Based on Information Entropy ». Advanced Materials Research 479-481 (février 2012) : 1592–95. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1592.

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Firstly, analysis product’s customer demand correlation, function correlation, geometric correlation, structure the corresponding correlation matrix, distribute the respective weighting factor, and then establish an integrated correlation matrix. Application of fuzzy clustering, the establishment of cluster map, the program has been divided into different modules. Based on information entropy theory, select product’s design and manufacturing complexity, cost, maintenance as the optimization objective, establish mathematical evaluation model of module division. Evaluating a number of options get from the fuzzy clustering method, which gain the most reasonable module division program. Finally, taking the seat frame of the Remote Control Weapon Station(RCWS) for example, verify the validity and reasonableness of the evaluation method.
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Thèses sur le sujet "Module clustering"

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Ptitsyn, Andrey. « New algorithms for EST clustering ». Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.
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Passmoor, Sean Stuart. « Clustering studies of radio-selected galaxies ». Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7521_1332410859.

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We investigate the clustering of HI-selected galaxies in the ALFALFA survey and compare results with those obtained for HIPASS. Measurements of the angular correlation function and the inferred 3D-clustering are compared with results from direct spatial-correlation measurements. We are able to measure clustering on smaller angular scales and for galaxies with lower HI masses than was previously possible. We calculate the expected clustering of dark matter using the redshift distributions of HIPASS and ALFALFA and show that the ALFALFA sample is somewhat more anti-biased with respect to dark matter than the HIPASS sample. We are able to conform the validity of the dark matter correlation predictions by performing simulations of the non-linear structure formation. Further we examine how the bias evolves with redshift for radio galaxies detected in the the first survey.

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Javar, Shima. « Measurement and comparison of clustering algorithms ». Thesis, Växjö University, School of Mathematics and Systems Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-1735.

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In this project, a number of different clustering algorithms are described and their workings explained. They are compared to each other by implementing them on number of graphs with a known architecture.

These clustering algorithm, in the order they are implemented, are as follows: Nearest neighbour hillclimbing, Nearest neighbour big step hillclimbing, Best neighbour hillclimbing, Best neighbour big step hillclimbing, Gem 3D, K-means simple, K-means Gem 3D, One cluster and One cluster per node.

The graphs are Unconnected, Directed KX, Directed Cycle KX and Directed Cycle.

The results of these clusterings are compared with each other according to three criteria: Time, Quality and Extremity of nodes distribution. This enables us to find out which algorithm is most suitable for which graph. These artificial graphs are then compared with the reference architecture graph to reach the conclusions.

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Hu, Yang. « PV Module Performance Under Real-world Test Conditions - A Data Analytics Approach ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396615109.

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Riedl, Pavel. « Modul shlukové analýzy systému pro dolování z dat ». Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237095.

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This master's thesis deals with development of a module for a data mining system, which is being developed on FIT. The first part describes the general knowledge discovery process and cluster analysis including cluster validation; it also describes Oracle Data Mining including algorithms, which it uses for clustering. At the end it deals with the system and the technologies it uses, such as NetBeans Platform and DMSL. The second part describes design of a clustering module and a module used to compare its results. It also deals with visualization of cluster analysis results and shows the achievements.
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Handfield, Louis-François. « Cis-regulatory modules clustering from sequence similarity ». Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=112632.

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I present a method that regroups cis-regulatory modules by shared sequences motifs. The goal of this approach is to search for clusters of modules that may share some function, using only sequence similarity. The proposed similarity measure is based on a variable-order Markov model likelihood scoring of sequences. I also introduce an extension of the variable-order Markov model which could better perform the required task. Results. I show that my method may recover subsets of sequences sharing a pattern in a set of generated sequences. I found that the proposed approach is successful in finding groups of modules that shared a type of transcription factor binding site.
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Wu, Jingwen. « Model-based clustering and model selection for binned data ». Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0005/document.

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Cette thèse étudie les approches de classification automatique basées sur les modèles de mélange gaussiens et les critères de choix de modèles pour la classification automatique de données discrétisées. Quatorze algorithmes binned-EM et quatorze algorithmes bin-EM-CEM sont développés pour quatorze modèles de mélange gaussiens parcimonieux. Ces nouveaux algorithmes combinent les avantages des données discrétisées en termes de réduction du temps d’exécution et les avantages des modèles de mélange gaussiens parcimonieux en termes de simplification de l'estimation des paramètres. Les complexités des algorithmes binned-EM et bin-EM-CEM sont calculées et comparées aux complexités des algorithmes EM et CEM respectivement. Afin de choisir le bon modèle qui s'adapte bien aux données et qui satisfait les exigences de précision en classification avec un temps de calcul raisonnable, les critères AIC, BIC, ICL, NEC et AWE sont étendus à la classification automatique de données discrétisées lorsque l'on utilise les algorithmes binned-EM et bin-EM-CEM proposés. Les avantages des différentes méthodes proposées sont illustrés par des études expérimentales
This thesis studies the Gaussian mixture model-based clustering approaches and the criteria of model selection for binned data clustering. Fourteen binned-EM algorithms and fourteen bin-EM-CEM algorithms are developed for fourteen parsimonious Gaussian mixture models. These new algorithms combine the advantages in computation time reduction of binning data and the advantages in parameters estimation simplification of parsimonious Gaussian mixture models. The complexities of the binned-EM and the bin-EM-CEM algorithms are calculated and compared to the complexities of the EM and the CEM algorithms respectively. In order to select the right model which fits well the data and satisfies the clustering precision requirements with a reasonable computation time, AIC, BIC, ICL, NEC, and AWE criteria, are extended to binned data clustering when the proposed binned-EM and bin-EM-CEM algorithms are used. The advantages of the different proposed methods are illustrated through experimental studies
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Sampson, Joshua Neil. « Clustering genes in genetical genomics / ». Thesis, Connect to this title online ; UW restricted, 2007. http://hdl.handle.net/1773/9549.

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Yelibi, Lionel. « Introduction to fast Super-Paramagnetic Clustering ». Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.

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We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters representative of standard economic sectors and mixed clusters whose dynamics shine light on the adaptive nature of financial markets and raise concerns relating to the effectiveness of industry based static financial market classification in the world of real-time data-analytics. A key result is that we show that the standard maximum likelihood methods are confirmed to converge to solutions within a Super-Paramagnetic (SP) phase. We use insights arising from this to discuss the implications of using a Maximum Entropy Principle (MEP) as opposed to the Maximum Likelihood Principle (MLP) as an optimization device for this class of problems.
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Mair, Patrick, et Marcus Hudec. « Session Clustering Using Mixtures of Proportional Hazards Models ». Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/598/1/document.pdf.

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Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various proportionality restrictions imposed. By introducing mixtures of Weibull proportional hazards models on a multivariate data set a parametric cluster approach based on the EM-algorithm is carried out. The problem of non-response in the data is considered. The application example is a real life data set stemming from the analysis of a world-wide operating eCommerce application. Sessions are clustered due to the dwell times a user spends on certain page-areas. The solution allows for the interpretation of the navigation behavior in terms of survival and hazard functions. A software implementation by means of an R package is provided. (author´s abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Livres sur le sujet "Module clustering"

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J, Davis Cecil, Herman Irving P et Turner Terry R, dir. Process module metrology, control, and clustering, 11-13 September 1991, San Jose, Calif. Bellingham, Wash : SPIE--International Society for Optical Engineering, 1991.

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J, Davis Cecil, Herman Irving P, Turner Terry R et Society of Photo-Optical Instrumentation Engineers., dir. Process module metrology, control, and clustering : 11-13 September 1991, San Jose, California. Bellingham, Wash : SPIE, 1992.

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Financial models with Levy processes and volatility clustering. Hoboken, N.J : Wiley, 2011.

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Wilson, Caroline L. Clustering algorithms and mathematical modeling. Hauppauge, N.Y : Nova Science Publishers, 2010.

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Rachev, S. T. Financial models with Lévy processes and volatility clustering. Hoboken, N.J : John Wiley, 2011.

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Sergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954- et DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), dir. Clustering challenges in biological networks. New Jersry : World Scientific, 2009.

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Sergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954- et DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), dir. Clustering challenges in biological networks. New Jersry : World Scientific, 2009.

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Tsangarides, Charalambos G. What is fuzzy about clustering in West Africa ? [Washington, D.C.] : International Monetary Fund, African Dept., 2006.

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MacCuish, John D. Clustering in bioinformatics and drug discovery. Boca Raton : Taylor & Francis, 2011.

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Grigor'ev, Anatoliy, et Evgeniy Isaev. Methods and algorithms of data processing. ru : INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects). The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms. Meets the requirements of Federal state educational standards of higher education of the last generation. For students of economic specialties, specialists, and graduate students.
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Chapitres de livres sur le sujet "Module clustering"

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Horvath, Steve. « Clustering Procedures and Module Detection ». Dans Weighted Network Analysis, 179–206. New York, NY : Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5_8.

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Paixao, Matheus, Mark Harman et Yuanyuan Zhang. « Multi-objective Module Clustering for Kate ». Dans Search-Based Software Engineering, 282–88. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22183-0_24.

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Yoshida, Ryo, Seiya Imoto et Tomoyuki Higuchi. « A Penalized Likelihood Estimation on Transcriptional Module-Based Clustering ». Dans Computational Science and Its Applications – ICCSA 2005, 389–401. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_42.

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Lecca, Paola, et Angela Re. « Module Detection in Dynamic Networks by Temporal Edge Weight Clustering ». Dans Computational Intelligence Methods for Bioinformatics and Biostatistics, 54–70. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44332-4_5.

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da Silva Júnior, Marcondes R., et Aluizio F. R. Araújo. « Subspace Clustering Multi-module Self-organizing Maps with Two-Stage Learning ». Dans Lecture Notes in Computer Science, 285–96. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_24.

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Liang, Dong, Jun Liu, Kuanquan Wang, Gongning Luo, Wei Wang et Shuo Li. « Position-Prior Clustering-Based Self-attention Module for Knee Cartilage Segmentation ». Dans Lecture Notes in Computer Science, 193–202. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16443-9_19.

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de Oliveira Barros, Márcio. « Evaluating Modularization Quality as an Extra Objective in Multiobjective Software Module Clustering ». Dans Search Based Software Engineering, 267. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23716-4_23.

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Ray, Sumanta, Sinchani Chakraborty et Anirban Mukhopadhyay. « DCoSpect : A Novel Differentially Coexpressed Gene Module Detection Algorithm Using Spectral Clustering ». Dans Advances in Intelligent Systems and Computing, 69–77. New Delhi : Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2695-6_7.

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Zamli, Kamal Z., Fakhrud Din, Nazirah Ramli et Bestoun S. Ahmed. « Software Module Clustering Based on the Fuzzy Adaptive Teaching Learning Based Optimization Algorithm ». Dans Intelligent and Interactive Computing, 167–77. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6031-2_3.

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Zainal, Nurul Asyikin, Kamal Z. Zamli et Fakhrud Din. « A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem ». Dans Lecture Notes in Electrical Engineering, 219–29. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2317-5_19.

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Actes de conférences sur le sujet "Module clustering"

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Seidel, Thomas E., et Michael R. Stark. « Learning opportunities through the use of cluster tools ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56617.

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Lally, Kevin. « Equipment improvement methodology ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56618.

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Seidel, J. P., W. Wachter, William M. Triggs et Robert P. Hall. « Integrated deposition of TiN barrier layers in cluster tools ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56619.

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Hauser, John R., et Syed A. Rizvi. « Cluster tool technology ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56620.

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Wong, Fred, et George E. Zilberman. « Open architecture cluster tool : communication and user interface integration ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56621.

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Boitnott, Charles A., et David R. Craven. « Single-wafer high-pressure oxidation ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56622.

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Hansen, Brad. « Benefits of cluster tool architecture for implementation of evolutionary equipment improvements and applications ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56623.

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Sitte, Renate, Sima Dimitrijev et H. Barry Harrison. « Dynamic design processing of integrated circuits for an "on target" end product ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56624.

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Ghatak, Kamakhya P. « Moss-Burstein shift in infrared materials under different physical conditions ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56625.

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Ghatak, Kamakhya P. « Photoemission from periodic structure of graded superlattices under magnetic field ». Dans Process Module Metrology, Control and Clustering, sous la direction de Cecil J. Davis, Irving P. Herman et Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56626.

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Rapports d'organisations sur le sujet "Module clustering"

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Oh, Man-Suk, et Adrian Raftery. Model-based Clustering with Dissimilarities : A Bayesian Approach. Fort Belvoir, VA : Defense Technical Information Center, décembre 2003. http://dx.doi.org/10.21236/ada459759.

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Merl, D. Advances in Bayesian Model Based Clustering Using Particle Learning. Office of Scientific and Technical Information (OSTI), novembre 2009. http://dx.doi.org/10.2172/1010386.

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De Leon, Phillip L., et Richard D. McClanahan. Efficient speaker verification using Gaussian mixture model component clustering. Office of Scientific and Technical Information (OSTI), avril 2012. http://dx.doi.org/10.2172/1039402.

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Bergman, O., et C. B. Thorn. Universality and clustering in 1 + 1 dimensional superstring-bit models. Office of Scientific and Technical Information (OSTI), mars 1996. http://dx.doi.org/10.2172/200667.

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Fraley, Chris, et Adrian E. Raftery. Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering. Fort Belvoir, VA : Defense Technical Information Center, août 2005. http://dx.doi.org/10.21236/ada454825.

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Fraley, Chris, Adrian Raftery et Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Fort Belvoir, VA : Defense Technical Information Center, décembre 2003. http://dx.doi.org/10.21236/ada459790.

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Wehrens, Ron, Lutgarde M. Buydens, Chris Fraley et Adrian E. Raftery. Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Fort Belvoir, VA : Defense Technical Information Center, février 2003. http://dx.doi.org/10.21236/ada459638.

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Murtagh, Fionn, Adrian E. Raftery et Jean-Luc Starck. Bayesian Inference for Color Image Quantization via Model-Based Clustering Trees. Fort Belvoir, VA : Defense Technical Information Center, novembre 2001. http://dx.doi.org/10.21236/ada459791.

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Fraley, Chris, et Adrian E. Raftery. MCLUST : Software for Model-Based Clustering, Density Estimation and Discriminant Analysis. Fort Belvoir, VA : Defense Technical Information Center, octobre 2002. http://dx.doi.org/10.21236/ada459792.

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Wang, Chih-Hao, et Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling ? Mineta Transportation Institute, juin 2021. http://dx.doi.org/10.31979/mti.2021.2011.

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The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above.
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