Literatura académica sobre el tema "Module clustering"
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Artículos de revistas sobre el tema "Module clustering"
Alshareef, Haya y Mashael Maashi. "Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem". Applied Sciences 12, n.º 11 (2 de junio de 2022): 5649. http://dx.doi.org/10.3390/app12115649.
Texto completoHou, Jie, Xiufen Ye, Chuanlong Li y Yixing Wang. "K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks". Genes 12, n.º 1 (12 de enero de 2021): 87. http://dx.doi.org/10.3390/genes12010087.
Texto completoHu, Hai Yan y You Qiao Zhang. "The Study and Realization of Energy-Aware Routing Algorithm of Wireless Sensor Networks". Applied Mechanics and Materials 201-202 (octubre de 2012): 767–72. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.767.
Texto completoKirve, Shraddha. "Clustering Techniques in Wireless Sensor Networks: A Practical Study". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (10 de junio de 2021): 536–38. http://dx.doi.org/10.22214/ijraset.2021.34990.
Texto completoKarayiannis, Dimitrios y Spyros Tragoudas. "Clustering Network Modules with Different Implementations for Delay Minimization". VLSI Design 7, n.º 1 (1 de enero de 1998): 1–13. http://dx.doi.org/10.1155/1998/69289.
Texto completoMohammad Shahid, Sunil Gupta y MS. Sofia Pillai. "Machine Learning-Based False Positive Software Vulnerability Analysis". Global Journal of Innovation and Emerging Technology 1, n.º 1 (15 de junio de 2022): 29–35. http://dx.doi.org/10.58260/j.iet.2202.0105.
Texto completoStrauch, Martin, Jochen Supper, Christian Spieth, Dierk Wanke, Joachim Kilian, Klaus Harter y 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, n.º 1 (1 de marzo de 2007): 81–93. http://dx.doi.org/10.1515/jib-2007-54.
Texto completoYu, Limin, Xianjun Shen, Jincai Yang, Kaiping Wei, Duo Zhong y Ruilong Xiang. "Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module". Evolutionary Bioinformatics 16 (enero de 2020): 117693432097057. http://dx.doi.org/10.1177/1176934320970572.
Texto completoAlam, M. K., Azrina Abd Aziz, S. A. Latif y Azlan Awang. "Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks". Sensors 20, n.º 4 (13 de febrero de 2020): 1011. http://dx.doi.org/10.3390/s20041011.
Texto completoWu, Yong Liang, Bao Quan Mao, Li Xu, Dong Ming Dai y Yan Chao Liu. "The Evaluation of Module Division Programme Based on Information Entropy". Advanced Materials Research 479-481 (febrero de 2012): 1592–95. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1592.
Texto completoTesis sobre el tema "Module clustering"
Ptitsyn, Andrey. "New algorithms for EST clustering". Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&.
Texto completoPassmoor, 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.
Texto completoWe 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.
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.
Texto completoIn 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.
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.
Texto completoRiedl, 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.
Texto completoHandfield, 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.
Texto completoWu, Jingwen. "Model-based clustering and model selection for binned data". Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0005/document.
Texto completoThis 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
Sampson, Joshua Neil. "Clustering genes in genetical genomics /". Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9549.
Texto completoYelibi, Lionel. "Introduction to fast Super-Paramagnetic Clustering". Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.
Texto completoMair, Patrick y 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.
Texto completoSeries: Research Report Series / Department of Statistics and Mathematics
Libros sobre el tema "Module clustering"
J, Davis Cecil, Herman Irving P y Turner Terry R, eds. Process module metrology, control, and clustering, 11-13 September 1991, San Jose, Calif. Bellingham, Wash: SPIE--International Society for Optical Engineering, 1991.
Buscar texto completoJ, Davis Cecil, Herman Irving P, Turner Terry R y Society of Photo-Optical Instrumentation Engineers., eds. Process module metrology, control, and clustering: 11-13 September 1991, San Jose, California. Bellingham, Wash: SPIE, 1992.
Buscar texto completoFinancial models with Levy processes and volatility clustering. Hoboken, N.J: Wiley, 2011.
Buscar texto completoWilson, Caroline L. Clustering algorithms and mathematical modeling. Hauppauge, N.Y: Nova Science Publishers, 2010.
Buscar texto completoRachev, S. T. Financial models with Lévy processes and volatility clustering. Hoboken, N.J: John Wiley, 2011.
Buscar texto completoSergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954- y DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), eds. Clustering challenges in biological networks. New Jersry: World Scientific, 2009.
Buscar texto completoSergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954- y DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), eds. Clustering challenges in biological networks. New Jersry: World Scientific, 2009.
Buscar texto completoTsangarides, Charalambos G. What is fuzzy about clustering in West Africa? [Washington, D.C.]: International Monetary Fund, African Dept., 2006.
Buscar texto completoE, MacCuish Norah, ed. Clustering in bioinformatics and drug discovery. Boca Raton: Taylor & Francis, 2011.
Buscar texto completoGrigor'ev, Anatoliy y Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.
Texto completoCapítulos de libros sobre el tema "Module clustering"
Horvath, Steve. "Clustering Procedures and Module Detection". En Weighted Network Analysis, 179–206. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5_8.
Texto completoPaixao, Matheus, Mark Harman y Yuanyuan Zhang. "Multi-objective Module Clustering for Kate". En Search-Based Software Engineering, 282–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22183-0_24.
Texto completoYoshida, Ryo, Seiya Imoto y Tomoyuki Higuchi. "A Penalized Likelihood Estimation on Transcriptional Module-Based Clustering". En Computational Science and Its Applications – ICCSA 2005, 389–401. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_42.
Texto completoLecca, Paola y Angela Re. "Module Detection in Dynamic Networks by Temporal Edge Weight Clustering". En 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.
Texto completoda Silva Júnior, Marcondes R. y Aluizio F. R. Araújo. "Subspace Clustering Multi-module Self-organizing Maps with Two-Stage Learning". En Lecture Notes in Computer Science, 285–96. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_24.
Texto completoLiang, Dong, Jun Liu, Kuanquan Wang, Gongning Luo, Wei Wang y Shuo Li. "Position-Prior Clustering-Based Self-attention Module for Knee Cartilage Segmentation". En Lecture Notes in Computer Science, 193–202. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16443-9_19.
Texto completode Oliveira Barros, Márcio. "Evaluating Modularization Quality as an Extra Objective in Multiobjective Software Module Clustering". En Search Based Software Engineering, 267. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23716-4_23.
Texto completoRay, Sumanta, Sinchani Chakraborty y Anirban Mukhopadhyay. "DCoSpect: A Novel Differentially Coexpressed Gene Module Detection Algorithm Using Spectral Clustering". En 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.
Texto completoZamli, Kamal Z., Fakhrud Din, Nazirah Ramli y Bestoun S. Ahmed. "Software Module Clustering Based on the Fuzzy Adaptive Teaching Learning Based Optimization Algorithm". En Intelligent and Interactive Computing, 167–77. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6031-2_3.
Texto completoZainal, Nurul Asyikin, Kamal Z. Zamli y Fakhrud Din. "A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem". En Lecture Notes in Electrical Engineering, 219–29. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2317-5_19.
Texto completoActas de conferencias sobre el tema "Module clustering"
Seidel, Thomas E. y Michael R. Stark. "Learning opportunities through the use of cluster tools". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56617.
Texto completoLally, Kevin. "Equipment improvement methodology". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56618.
Texto completoSeidel, J. P., W. Wachter, William M. Triggs y Robert P. Hall. "Integrated deposition of TiN barrier layers in cluster tools". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56619.
Texto completoHauser, John R. y Syed A. Rizvi. "Cluster tool technology". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56620.
Texto completoWong, Fred y George E. Zilberman. "Open architecture cluster tool: communication and user interface integration". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56621.
Texto completoBoitnott, Charles A. y David R. Craven. "Single-wafer high-pressure oxidation". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56622.
Texto completoHansen, Brad. "Benefits of cluster tool architecture for implementation of evolutionary equipment improvements and applications". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56623.
Texto completoSitte, Renate, Sima Dimitrijev y H. Barry Harrison. "Dynamic design processing of integrated circuits for an "on target" end product". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56624.
Texto completoGhatak, Kamakhya P. "Moss-Burstein shift in infrared materials under different physical conditions". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56625.
Texto completoGhatak, Kamakhya P. "Photoemission from periodic structure of graded superlattices under magnetic field". En Process Module Metrology, Control and Clustering, editado por Cecil J. Davis, Irving P. Herman y Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56626.
Texto completoInformes sobre el tema "Module clustering"
Oh, Man-Suk y Adrian Raftery. Model-based Clustering with Dissimilarities: A Bayesian Approach. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2003. http://dx.doi.org/10.21236/ada459759.
Texto completoMerl, D. Advances in Bayesian Model Based Clustering Using Particle Learning. Office of Scientific and Technical Information (OSTI), noviembre de 2009. http://dx.doi.org/10.2172/1010386.
Texto completoDe Leon, Phillip L. y Richard D. McClanahan. Efficient speaker verification using Gaussian mixture model component clustering. Office of Scientific and Technical Information (OSTI), abril de 2012. http://dx.doi.org/10.2172/1039402.
Texto completoBergman, O. y C. B. Thorn. Universality and clustering in 1 + 1 dimensional superstring-bit models. Office of Scientific and Technical Information (OSTI), marzo de 1996. http://dx.doi.org/10.2172/200667.
Texto completoFraley, Chris y Adrian E. Raftery. Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2005. http://dx.doi.org/10.21236/ada454825.
Texto completoFraley, Chris, Adrian Raftery y Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2003. http://dx.doi.org/10.21236/ada459790.
Texto completoWehrens, Ron, Lutgarde M. Buydens, Chris Fraley y Adrian E. Raftery. Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Fort Belvoir, VA: Defense Technical Information Center, febrero de 2003. http://dx.doi.org/10.21236/ada459638.
Texto completoMurtagh, Fionn, Adrian E. Raftery y Jean-Luc Starck. Bayesian Inference for Color Image Quantization via Model-Based Clustering Trees. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 2001. http://dx.doi.org/10.21236/ada459791.
Texto completoFraley, Chris y Adrian E. Raftery. MCLUST: Software for Model-Based Clustering, Density Estimation and Discriminant Analysis. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2002. http://dx.doi.org/10.21236/ada459792.
Texto completoWang, Chih-Hao y Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, junio de 2021. http://dx.doi.org/10.31979/mti.2021.2011.
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