Literatura académica sobre el tema "Cluster clustering"
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Artículos de revistas sobre el tema "Cluster clustering":
Ahamad, Mohammed Gulam, Mohammed Faisal Ahmed y Mohammed Yousuf Uddin. "Clustering as Data Mining Technique in Risk Factors Analysis of Diabetes, Hypertension and Obesity." European Journal of Engineering and Technology Research 1, n.º 6 (27 de julio de 2018): 88–93. http://dx.doi.org/10.24018/ejeng.2016.1.6.202.
Barnes, J., A. Dekel, G. Efstathiou y C. S. Frenk. "Cluster-cluster clustering". Astrophysical Journal 295 (agosto de 1985): 368. http://dx.doi.org/10.1086/163381.
Rosing, K. E. y C. S. ReVelle. "Optimal Clustering". Environment and Planning A: Economy and Space 18, n.º 11 (noviembre de 1986): 1463–76. http://dx.doi.org/10.1068/a181463.
Popkov, Yuri S., Yuri A. Dubnov y Alexey Yu Popkov. "Entropy-Randomized Clustering". Mathematics 10, n.º 19 (10 de octubre de 2022): 3710. http://dx.doi.org/10.3390/math10193710.
Baisya, Ritasman, Phani Kumar Devarasetti, Murthy G. S. R. y Liza Rajasekhar. "Autoantibody Clustering in Systemic Lupus Erythematosus–Associated Pulmonary Arterial Hypertension". Indian Journal of Cardiovascular Disease in Women - WINCARS 06, n.º 02 (abril de 2021): 100–105. http://dx.doi.org/10.1055/s-0041-1732510.
Alfian, Muhammad, Ali Ridho Barakbah y Idris Winarno. "Indonesian Online News Extraction and Clustering Using Evolving Clustering". JOIV : International Journal on Informatics Visualization 5, n.º 3 (23 de septiembre de 2021): 280. http://dx.doi.org/10.30630/joiv.5.3.537.
Cornell, John E., Jacqueline A. Pugh, John W. Williams, Jr, Lewis Kazis, Austin F. S. Lee, Michael L. Parchman, John Zeber, Thomas Pederson, Kelly A. Montgomery y Polly Hitchcock Noël. "Multimorbidity Clusters: Clustering Binary Data From Multimorbidity Clusters: Clustering Binary Data From a Large Administrative Medical Database". Applied Multivariate Research 12, n.º 3 (13 de enero de 2009): 163. http://dx.doi.org/10.22329/amr.v12i3.658.
Li, Hong-Dong, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn y Jianxin Wang. "ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets". Journal of Bioinformatics and Computational Biology 18, n.º 03 (junio de 2020): 2040009. http://dx.doi.org/10.1142/s0219720020400090.
BORGELT, CHRISTIAN. "RESAMPLING FOR FUZZY CLUSTERING". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, n.º 05 (octubre de 2007): 595–614. http://dx.doi.org/10.1142/s0218488507004893.
Miyamoto, Sadaaki, Youhei Kuroda y Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering". Journal of Advanced Computational Intelligence and Intelligent Informatics 12, n.º 5 (20 de septiembre de 2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.
Tesis sobre el tema "Cluster clustering":
Dimitriadou, Evgenia, Andreas Weingessel y Kurt Hornik. "A voting-merging clustering algorithm". SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/94/1/document.pdf.
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Al-Razgan, Muna Saleh. "Weighted clustering ensembles". Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.
Vita: 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.
Gaertler, Marco. "Clustering with spectral methods". [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10101213.
Ptitsyn, Andrey. "New algorithms for EST clustering". Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&.
Koepke, Hoyt Adam. "Bayesian cluster validation". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1496.
Tittley, Eric Robert. "Hierarchical clustering and galaxy cluster scaling laws". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0008/NQ40291.pdf.
Kuah, Adrian T. H. "Determinants of clustering, cluster growth and performance". Thesis, University of Manchester, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629921.
Shortreed, Susan. "Learning in spectral clustering /". Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8977.
Chan, Alton Kam Fai. "Hyperplane based efficient clustering and searching /". View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CHANA.
Madureira, Erikson Manuel Geraldo Vieira de. "Análise de mercado : clustering". Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/13122.
O presente trabalho tem como objetivo descrever as atividades realizadas durante o estágio efetuado na empresa Quidgest. Tendo a empresa a necessidade de estudar as suas diversas vertentes de negócio, optou-se por extrair e identificar as informações presentes no banco de dados da empresa. Para isso, foi utilizado um processo conhecido na análise de dados denominado por Extração de Conhecimento em Bases de Dados (ECBD). O maior desafio na utilização deste processo deveu-se há grande acumulação de informação pela empresa, que se foi intensificando a partir de 2013. Das fases do processo de ECBD, a que tem maior relevância é o data mining, onde é feito um estudo das variáveis caracterizadoras necessárias para a análise em foco. Foi escolhida a técnica de análise cluster da fase de data mining para que que toda análise possa ser eficiente, eficaz e se possa obter resultados de fácil leitura. Após o desenvolvimento do processo de ECBD, foi decidido que a fase de data mining podia ser implementada de modo a facilitar um trabalho futuro de uma análise realizada pela empresa. Para implementar essa fase, utilizaram-se técnicas de análise cluster e foi desenvolvida um programa em VBA/Excel centrada no utilizador. Para testar o programa criado foi utilizado um caso concreto da empresa. Esse caso consistiu em determinar quais os atuais clientes que mais contribuíram para a evolução da empresa nos anos de 2013 a 2015. Aplicando o caso referido no programa criado, obtiveram-se resultados e informações que foram analisadas e interpretadas.
This paper aims to describe the activities performed during the internship made in Quidgest company. Having the company need to study their various business areas, it was decided to extract and identify the information contained in the company's database. For this end, we used a process known in the data analysis called for Knowledge Discovery in Databases (KDD). The biggest challenge in using this process was due to their large accumulation of information by the company, which was intensified from 2013. The phases of the KDD process, which is the most relevant is data mining, where a study of characterizing variables required for the analysis is done. The cluster analysis technique of data mining phase was chosen for that any analysis can be efficient, effective and could provide results easy to read. After the development of the KDD process, it was decided that the data mining phase could be automated to facilitate future work carried out by the company. To automate this phase, cluster analysis techniques were used and was developed a program in VBA/Excel user-centered. To test the created program we used a specific case of the company. This case consisted in determining the current customers that have contributed to the company's evolution during the years 2013-2015. The application of the program has revealed useful information that has been analyzed and interpreted.
info:eu-repo/semantics/publishedVersion
Libros sobre el tema "Cluster clustering":
Xu, Rui. Clustering. Hoboken, N.J: Wiley, 2009.
Phipps, Arabie, Hubert Lawrence J. 1944- y Soete Geert de, eds. Clustering and classification. Singapore: World Scientific, 1996.
Murtagh, Fionn. Multidimensional clustering algorithms. Vienna: Physica-Verlag, 1985.
Miyamoto, Sadaaki. Algorithms for fuzzy clustering: Methods in c-means clustering with applications. Berlin: Springer, 2008.
Das, Swagatam. Metaheuristic clustering. Berlin: Springer, 2009.
Jain, Anil K. Algorithms for clustering data. Englewood Cliffs, N.J: Prentice Hall, 1988.
Mirkin, B. G. Mathematical classification and clustering. Dordrecht: Kluwer Academic Publishers, 1996.
Janowitz, M. F. Ordinal and relational clustering. Singapore: World Scientific, 2010.
Sergiy, 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.
Sergiy, 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.
Capítulos de libros sobre el tema "Cluster clustering":
Govaert, Gérard y Mohamed Nadif. "Cluster Analysis". En Co-Clustering, 1–53. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118649480.ch1.
Scitovski, Rudolf, Kristian Sabo, Francisco Martínez-Álvarez y Šime Ungar. "Data Clustering". En Cluster Analysis and Applications, 31–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74552-3_3.
Mirkin, Boris. "Single Cluster Clustering". En Nonconvex Optimization and Its Applications, 169–227. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0457-9_4.
Price, P. B. "Cluster Radioactivity". En Clustering Phenomena in Atoms and Nuclei, 273–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02827-8_28.
Bezdek, James C. "Probabilistic Clustering - GMD/EM". En Elementary Cluster Analysis, 193–223. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-9.
Scitovski, Rudolf, Kristian Sabo, Francisco Martínez-Álvarez y Šime Ungar. "Fuzzy Clustering Problem". En Cluster Analysis and Applications, 147–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74552-3_7.
Scitovski, Rudolf, Kristian Sabo, Francisco Martínez-Álvarez y Šime Ungar. "Mahalanobis Data Clustering". En Cluster Analysis and Applications, 117–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74552-3_6.
Săndulescu, A., A. Ludu y W. Greiner. "Cluster Radioactivity and Nuclear Structure-Clusters as Solitons". En Clustering Phenomena in Atoms and Nuclei, 262–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02827-8_27.
Bezdek, James C. "Relational Clustering - The SAHN Models". En Elementary Cluster Analysis, 225–64. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-10.
Bezdek, James C. "Clustering in Static Big Data". En Elementary Cluster Analysis, 325–78. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-13.
Actas de conferencias sobre el tema "Cluster clustering":
Hauser, 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.
Emeneker, Wesley y Dan Stanzione. "Dynamic Virtual Clustering". En 2007 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2007. http://dx.doi.org/10.1109/clustr.2007.4629220.
Zhang, Hongjing y Ian Davidson. "Deep Descriptive Clustering". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/460.
Venkataraman, P. "Data Clustering Using the Natural Bézier Functions". En ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85103.
Arıcıoğlu, Mustafa Atilla y Yasemin Savaş. "Clustering Policies in Japan as an Example of Clustering Strategy". En International Conference on Eurasian Economies. Eurasian Economists Association, 2021. http://dx.doi.org/10.36880/c13.02567.
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.
Li, Jia, Dongsheng Li y Yiming Zhang. "Efficient Distributed Data Clustering on Spark". En 2015 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2015. http://dx.doi.org/10.1109/cluster.2015.84.
Dey, Sayak, Swagatam Das y Rammohan Mallipeddi. "The Sparse MinMax k-Means Algorithm for High-Dimensional Clustering". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/291.
Zhao, Han, Xu Yang, Zhenru Wang, Erkun Yang y Cheng Deng. "Graph Debiased Contrastive Learning with Joint Representation Clustering". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/473.
Seidel, 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.
Informes sobre el tema "Cluster clustering":
Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk y Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], octubre de 2020. http://dx.doi.org/10.31812/123456789/4470.
Ravindran, Vijay y Chockalingam Vannila. An Energy-efficient Clustering Protocol for IoT Wireless Sensor Networks Based on Cluster Supervisor Management. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, diciembre de 2021. http://dx.doi.org/10.7546/crabs.2021.12.12.
Wang, Li, Vu Li, Huang Deng y Chu Pan. Existence non-commutative clustering methods for optimizing a load of processor cores for multiple marking of percolation cluster algorithm. Web of Open Science, febrero de 2020. http://dx.doi.org/10.37686/ser.v1i1.3.
Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, diciembre de 2020. http://dx.doi.org/10.37686/ser.v1i2.79.
Rutherford, J. y J. F. Cassidy. Comparing felt intensity patterns for crustal earthquakes in the Cascadia and Chilean subduction zones, offshore British Columbia, United States, and Chile. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330475.
Fraley, 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.
Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, Manlio De Domenico, Giuseppe Mangioni, Simone Righi y Annamaria Simonazzi. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, enero de 2022. http://dx.doi.org/10.36687/inetwp173.
DAVID A. BOOTHMAN, Ph D. Clusterin: an IR-inducible protein determining life and death. Office of Scientific and Technical Information (OSTI), julio de 2006. http://dx.doi.org/10.2172/886107.
Leskov, Konstantin S. y David A. Boothman. The Role of Clusterin in Estrogen Deprivation-Mediated Cell Death in Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, julio de 2002. http://dx.doi.org/10.21236/ada407480.
Criswell, Tracy L. y David A. Boothman. Investigating the Role of Nuclear Clusterin (nCLU) in Lethality and Genomic Instability in Paclitaxel (Taxol) - Treated Human Breast Cancer Cells. Fort Belvoir, VA: Defense Technical Information Center, julio de 2002. http://dx.doi.org/10.21236/ada406785.