Littérature scientifique sur le sujet « Clustering analysi »
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Articles de revues sur le sujet "Clustering analysi"
Jadhav, Priyanka, et Rasika Patil. « Analysis of Clustering technique ». International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30 juin 2018) : 2422–24. http://dx.doi.org/10.31142/ijtsrd15616.
Texte intégralManjunath, Mohith, Yi Zhang, Yeonsung Kim, Steve H. Yeo, Omar Sobh, Nathan Russell, Christian Followell, Colleen Bushell, Umberto Ravaioli et Jun S. Song. « ClusterEnG : an interactive educational web resource for clustering and visualizing high-dimensional data ». PeerJ Computer Science 4 (21 mai 2018) : e155. http://dx.doi.org/10.7717/peerj-cs.155.
Texte intégralFisher, D. « Iterative Optimization and Simplification of Hierarchical Clusterings ». Journal of Artificial Intelligence Research 4 (1 avril 1996) : 147–78. http://dx.doi.org/10.1613/jair.276.
Texte intégralPatel, Khushbu. « Analysis of Various Database Using Clustering Techniques ». Global Journal For Research Analysis 3, no 7 (15 juin 2012) : 59–60. http://dx.doi.org/10.15373/22778160/july2014/20.
Texte intégralDavidson, Ian, et S. S. Ravi. « Making Existing Clusterings Fairer : Algorithms, Complexity Results and Insights ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 04 (3 avril 2020) : 3733–40. http://dx.doi.org/10.1609/aaai.v34i04.5783.
Texte intégralVEGA-PONS, SANDRO, et JOSÉ RUIZ-SHULCLOPER. « A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS ». International Journal of Pattern Recognition and Artificial Intelligence 25, no 03 (mai 2011) : 337–72. http://dx.doi.org/10.1142/s0218001411008683.
Texte intégralMadhuri, K., et Mr K. Srinivasa Rao. « Social Media Analysis using Optimized K-Means Clustering ». International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (28 février 2019) : 953–57. http://dx.doi.org/10.31142/ijtsrd21558.
Texte intégralLi, Hong-Dong, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn et Jianxin Wang. « ClusterMine : A knowledge-integrated clustering approach based on expression profiles of gene sets ». Journal of Bioinformatics and Computational Biology 18, no 03 (juin 2020) : 2040009. http://dx.doi.org/10.1142/s0219720020400090.
Texte intégralWang, Xing, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guoqiang Xiao et Maozu Guo. « Multiple Independent Subspace Clusterings ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 5353–60. http://dx.doi.org/10.1609/aaai.v33i01.33015353.
Texte intégralKerdprasop, Nittaya, Kacha Chansilp et Kittisak Kerdprasop. « Greenness Pattern Analysis with the Remote Sensing Index Clustering ». International Journal of Machine Learning and Computing 7, no 6 (décembre 2017) : 181–86. http://dx.doi.org/10.18178/ijmlc.2017.7.6.643.
Texte intégralThèses sur le sujet "Clustering analysi"
Zreik, Rawya. « Analyse statistique des réseaux et applications aux sciences humaines ». Thesis, Paris 1, 2016. http://www.theses.fr/2016PA01E061/document.
Texte intégralOver the last two decades, network structure analysis has experienced rapid growth with its construction and its intervention in many fields, such as: communication networks, financial transaction networks, gene regulatory networks, disease transmission networks, mobile telephone networks. Social networks are now commonly used to represent the interactions between groups of people; for instance, ourselves, our professional colleagues, our friends and family, are often part of online networks, such as Facebook, Twitter, email. In a network, many factors can exert influence or make analyses easier to understand. Among these, we find two important ones: the time factor, and the network context. The former involves the evolution of connections between nodes over time. The network context can then be characterized by different types of information such as text messages (email, tweets, Facebook, posts, etc.) exchanged between nodes, categorical information on the nodes (age, gender, hobbies, status, etc.), interaction frequencies (e.g., number of emails sent or comments posted), and so on. Taking into consideration these factors can lead to the capture of increasingly complex and hidden information from the data. The aim of this thesis is to define new models for graphs which take into consideration the two factors mentioned above, in order to develop the analysis of network structure and allow extraction of the hidden information from the data. These models aim at clustering the vertices of a network depending on their connection profiles and network structures, which are either static or dynamically evolving. The starting point of this work is the stochastic block model, or SBM. This is a mixture model for graphs which was originally developed in social sciences. It assumes that the vertices of a network are spread over different classes, so that the probability of an edge between two vertices only depends on the classes they belong to
Karim, Ehsanul, Sri Phani Venkata Siva Krishna Madani et Feng Yun. « Fuzzy Clustering Analysis ». Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2165.
Texte intégralAl-Razgan, Muna Saleh. « Weighted clustering ensembles ». Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.
Texte intégralVita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.
Leisch, Friedrich. « Bagged clustering ». SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1272/1/document.pdf.
Texte intégralSeries: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Gupta, Pramod. « Robust clustering algorithms ». Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39553.
Texte intégralXu, Tianbing. « Nonparametric evolutionary clustering ». Diss., Online access via UMI:, 2009.
Trouver le texte intégralShortreed, Susan. « Learning in spectral clustering / ». Thesis, Connect to this title online ; UW restricted, 2006. http://hdl.handle.net/1773/8977.
Texte intégralPtitsyn, Andrey. « New algorithms for EST clustering ». Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&.
Texte intégralKarimi, Kambiz. « Clustering analysis of residential loads ». Kansas State University, 2016. http://hdl.handle.net/2097/32616.
Texte intégralDepartment of Electrical and Computer Engineering
Anil Pahwa
Understanding electricity consumer behavior at different times of the year and throughout the day is very import for utilities. Though electricity consumers pay a fixed predetermined amount of money for using electric energy, the market wholesale prices vary hourly during the day. This analysis is intended to see overall behavior of consumers in different seasons of the year and compare them with the market wholesale prices. Specifically, coincidence of peaks in the loads with peak of market wholesale price is analyzed. This analysis used data from 101 homes in Austin, TX, which are gathered and stored by Pecan Street Inc. These data were used to first determine the average seasonal load profiles of all houses. Secondly, the houses were categorized into three clusters based on similarities in the load profiles using k-means clustering method. Finally, the average seasonal profiles of each cluster with the wholesale market prices which was taken from Electric Reliability Council of Texas (ERCOT) were compared. The data obtained for the houses were in 15-min intervals so they were first changed to average hourly profiles. All the data were then used to determine average seasonal profiles for each house in each season (winter, spring, summer and fall). We decided to set three levels of clusters). All houses were then categorized into one of these three clusters using k-means clustering. Similarly electricity prices taken from ERCOT, which were also on 15-min basis, were changed to hourly averages and then to seasonal averages. Through clustering analysis we found that a low percent of the consumers did not change their pattern of electricity usage while the majority of the users changed their electricity usage pattern once from one season to another. This change in usage patterns mostly depends on level of income, type of heating and cooling systems used, and other electric appliances used. Comparing the ERCOT prices with the average seasonal electricity profiles of each cluster we found that winter and spring seasons are critical for utilities and the ERCOT price peaks in the morning while the peak loads occur in the evening. In summer and fall, on the other hand, ERCOT price and load demand peak at almost the same time with one or two hour difference. This analysis can help utilities and other authorities make better electricity usage policies so they could shift some of the load from the time of peak to other times.
FARMANI, MOHAMMAD REZA. « Clustering analysis using Swarm Intelligence ». Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266871.
Texte intégralLivres sur le sujet "Clustering analysi"
Xu, Rui. Clustering. Hoboken, N.J : Wiley, 2009.
Trouver le texte intégralMirkin, B. G. Mathematical classification and clustering. Dordrecht : Kluwer Academic Publishers, 1996.
Trouver le texte intégralPhipps, Arabie, Hubert Lawrence J. 1944- et Soete Geert de, dir. Clustering and classification. Singapore : World Scientific, 1996.
Trouver le texte intégral1968-, Abraham Ajith, et Konar Amit, dir. Metaheuristic clustering. Berlin : Springer, 2009.
Trouver le texte intégralMurtagh, Fionn. Multidimensional clustering algorithms. Vienna : Physica-Verlag, 1985.
Trouver le texte intégralMiyamoto, Sadaaki. Algorithms for fuzzy clustering : Methods in c-means clustering with applications. Berlin : Springer, 2008.
Trouver le texte intégralJajuga, Krzysztof, Andrzej Sokołowski et Hans-Hermann Bock, dir. Classification, Clustering, and Data Analysis. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8.
Texte intégralKusiak, Andrew. Clustering analysis : Models and algorithms. [Urbana, Ill.] : College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1985.
Trouver le texte intégralC, Dubes Richard, dir. Algorithms for clustering data. Englewood Cliffs, N.J : Prentice Hall, 1988.
Trouver le texte intégralE, Alexander F., et Boyle P, dir. Methods for investigating localized clustering of disease. Lyon, France : International Agency for Research on Cancer, World Health Organization, 1996.
Trouver le texte intégralChapitres de livres sur le sujet "Clustering analysi"
Govaert, Gérard, et Mohamed Nadif. « Cluster Analysis ». Dans Co-Clustering, 1–53. Hoboken, USA : John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118649480.ch1.
Texte intégralGaertler, Marco. « Clustering ». Dans Network Analysis, 178–215. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31955-9_8.
Texte intégralBolshoy, Alexander, Zeev (Vladimir) Volkovich, Valery Kirzhner et Zeev Barzily. « Mathematical Models for the Analysis of Natural-Language Documents ». Dans Genome Clustering, 23–42. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12952-0_3.
Texte intégralOlive, David J. « Clustering ». Dans Robust Multivariate Analysis, 385–91. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68253-2_13.
Texte intégralL. Jockers, Matthew, et Rosamond Thalken. « Clustering ». Dans Text Analysis with R, 177–94. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_15.
Texte intégralWindham, Michael P. « Robust Clustering ». Dans Data Analysis, 385–92. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_31.
Texte intégralBagirov, Adil M., et Ehsan Mohebi. « Nonsmooth Optimization Based Algorithms in Cluster Analysis ». Dans Partitional Clustering Algorithms, 99–146. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09259-1_4.
Texte intégralPhillips, Jeff M. « Clustering ». Dans Mathematical Foundations for Data Analysis, 177–205. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62341-8_8.
Texte intégralBillard, Lynne, et Edwin Diday. « Symbolic Regression Analysis ». Dans Classification, Clustering, and Data Analysis, 281–88. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_31.
Texte intégralBatagelj, Vladimir, et Anuška Ferligoj. « Clustering Relational Data ». Dans Data Analysis, 3–15. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_1.
Texte intégralActes de conférences sur le sujet "Clustering analysi"
Ramanujachar, Kartik, et Satish Draksharam. « Note on the Use of Principal Component Analysis (PCA) and Clustering for the Analysis of Wafer Level ATPG data ». Dans ISTFA 2006. ASM International, 2006. http://dx.doi.org/10.31399/asm.cp.istfa2006p0219.
Texte intégralEslahchi, Changiz, Mehdi Sadeghi, Hamid Pezeshk, Mehdi Kargar, Hadi Poormohammadi, Theodore E. Simos, George Psihoyios et Ch Tsitouras. « Haplotyping Problem, A Clustering Approach ». Dans Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790104.
Texte intégralAfonso, Carlos, Fábio Ferreira, José Exposto et Ana I. Pereira. « Comparing clustering and partitioning strategies ». Dans NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012 : International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756254.
Texte intégralSalgado, Paulo, Lio Gonçalves, Getúlio Igrejas, Theodore E. Simos, George Psihoyios, Ch Tsitouras et Zacharias Anastassi. « Sliding PCA Fuzzy Clustering Algorithm ». Dans NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011 : International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637005.
Texte intégralBraginsky, Michael, et Valeriy Buryachenko. « Transformation Field Analysis in Clustering Discretization Method in Micromechanics of Random Structure Composites ». Dans ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95138.
Texte intégralAlasti, Aria, Hassan Salarieh et Rasool Shabani. « Sliding Mode Control of Electromagnetic System Based on Fuzzy Clustering Estimation : An Experimental Study ». Dans ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58442.
Texte intégralAkbas, Esra, et Peixiang Zhao. « Attributed Graph Clustering ». Dans ASONAM '17 : Advances in Social Networks Analysis and Mining 2017. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3110025.3110092.
Texte intégralDinu, Liviu P., et Denis Enăchescu. « On clustering Romance languages ». Dans Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0061.
Texte intégralArıcıoğlu, Mustafa Atilla, Muhittin Koraş et Mustafa Gömleksiz. « Competitiveness Analysis of the Konya Footwear Cluster ». Dans International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.01134.
Texte intégralHunter, Blake, Thomas Strohmer, Theodore E. Simos, George Psihoyios et Ch Tsitouras. « Compressive Spectral Clustering ». Dans ICNAAM 2010 : International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3498187.
Texte intégralRapports d'organisations sur le sujet "Clustering analysi"
Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk et Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], octobre 2020. http://dx.doi.org/10.31812/123456789/4470.
Texte intégralChen, Maximillian Gene, Kristin Marie Divis, James D. Morrow et Laura A. McNamara. Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data. Office of Scientific and Technical Information (OSTI), septembre 2018. http://dx.doi.org/10.2172/1472228.
Texte intégralMartone, Anthony, Roberto Innocenti et Kenneth Ranney. An Analysis of Clustering Tools for Moving Target Indication. Fort Belvoir, VA : Defense Technical Information Center, novembre 2009. http://dx.doi.org/10.21236/ada512473.
Texte intégralKanungo, T., D. M. Mount, N. S. Netanyahu, C. Piatko, R. Silverman et A. Y. Wu. The Analysis of a Simple k-Means Clustering Algorithm. Fort Belvoir, VA : Defense Technical Information Center, janvier 2000. http://dx.doi.org/10.21236/ada458738.
Texte intégralFraley, 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.
Texte intégralCordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, décembre 2020. http://dx.doi.org/10.37686/ser.v1i2.79.
Texte intégralHarris, J. Clustering of gamma ray spectrometer data using a computer image analysis system. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128043.
Texte intégralChoudhary, Alok, Ankit Agrawal et Wei-Keng Liao. Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing. Office of Scientific and Technical Information (OSTI), juillet 2021. http://dx.doi.org/10.2172/1896359.
Texte intégralHehr, Brian Douglas. LDRD Report : Analysis of Defect Clustering in Semiconductors using Kinetic Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), janvier 2014. http://dx.doi.org/10.2172/1465520.
Texte intégralPerr-Sauer, Jordan, Adam W. Duran et Caleb T. Phillips. Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data. Office of Scientific and Technical Information (OSTI), janvier 2020. http://dx.doi.org/10.2172/1597242.
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